Explore the vast range of titles available.

Given a graph G(V,E), where V is the set of vertices and E is the set of edges connecting vertices in V, and for every edge eij there is an associated weight cij ≥0, The Multi Period Degree Constrained Minimum Spanning Tree (MPDCMST) is a problem of finding an MST while also considering the degree constrained on every vertex, and satisfying vertices installation on every period. The restriction on the vertex installation is needed due to some conditions such as fund limitation, harsh weather, and so on. In this research some algorithms developed to solve the MPDCMST Problem will be discussed and compared.

In this paper, we show the existence of the perfect foresight forecast functions which generate unique and stable equilibrium price dynamics for comparative static analysis and calibration in multi-period overlapping generations models with cohort heterogeneity, multiple goods, and fiat money. We also show how to recover such forecast functions up to the first order using the eigenvalues and eigenvectors of the Jacobian matrix of the equilibrium conditions. In a special one good case, our construction of the forecast functions requires information about only the eigenvalues. In addition, we demonstrate that the stable subspace where the price sequences generated by the linearized forecast functions exist is also an invariant cyclic subspace.

This paper considers repositioning empty containers between multi-ports over multi-periods with stochastic demand and lost sales. The objective is to minimize the total operating cost including container-holding cost, stockout cost, importing cost and exporting cost. First, we formulate the single-port case as an inventory problem over a finite horizon with stochastic import and export of empty containers. The optimal policy for period n is characterized by a pair of critical points (A n , S n ), that is, importing empty containers up to A n when the number of empty containers in the port is fewer than A n ; exporting empty containers down to S n when the number of empty containers in the port is more than S n ; and doing nothing, otherwise. A polynomial-time algorithm is developed to determine the two thresholds, that is, A n and S n , for each period. Next, we formulate the multi-port problem and determine a tight lower bound on the cost function. On the basis of the two-threshold optimal policy for a single port, a polynomial-time algorithm is developed to find an approximate repositioning policy for multi-ports. Simulation results show that the proposed approximate repositioning algorithm performs very effectively and efficiently.

The routing problems are one of the most important problems in the field of logistic with great practical applicability. This article deals with synchronized distribution during time periods. It is based on optimization using classical capacitated vehicle routing problem while providing different customers’ demands among time periods. The goal is, except the minimization of the total distance, to achieve the stability of solution among the time periods, which contributes to simplification of transportation planning. Although using of the presented model can lead to a partial increase of distribution cost, on the other hand it ensures more transparent routes and eliminates the need for daily optimization. Therefore it brings a positive effect for distributors, drivers and customers that may result in greater economic benefits than the benefit from the optimization on a daily basis.

To cope with climate change and achieve sustainable development, low-carbon city pilot policies have been implemented. An objective assessment of the performance of these policies facilitates not only the implementation of relevant work in pilot areas, but also the further promotion of these policies. This study uses A-share listed enterprises from 2005 to 2019 and creates a multi-period difference-in-differences model to explore the impact of low-carbon city pilot policies on corporate green technology innovation from multiple dimensions. Results show that (1) low-carbon city pilot policies stimulates the green technological innovation of enterprises as manifested in their application of green invention patents; (2) the introduction of pilot policies is highly conducive to green technological innovation in eastern cities and enterprises in high-carbon emission industries; and (3) tax incentives and government subsidies are important fiscal and taxation tools that play the role of pilot policies in low-carbon cities. By alleviating corporate financing constraints, these policies effectively promote the green technological innovation of enterprises. This study expands the research on the performance of low-carbon city pilot policies and provides data support for a follow-up implementation and promotion of policies from the micro perspective at the enterprise level.

Green bonds, a new green financial instrument, encourage enterprises to achieve high-quality development through green technology innovation. However, a lack of research is currently being conducted into the effect of green bond issuance in China. Can green bonds effectively empower enterprises to green innovation? What is the underlying mechanism? In the context of carbon-neutral strategies, it is significant to answer these questions scientifically. This paper uses a quasi-natural experiment of the launch of the green bond market in China in 2016 to conduct empirical studies based on the panel data of 1 558 non-financial Chinese-listed enterprises from 2015 to 2020 with the multi-period difference-in-difference model. The results show that ① issuing green bonds can significantly empower enterprises’ green technology innovation. The empowering effect is mainly for green utility patents rather than green invention patents. This result remains after dynamic heterogeneity analysis, placebo test, and other tests. In addition, the effect has a lag. ② Heterogeneity tests show that this empowerment effect varies across enterprises with different property rights, industries, and regions. ③ In terms of the mechanism of action, green bonds can enhance enterprises’ ability to innovate green technology by increasing the proportion of long-term loans and improving their debt structure. This paper broadens the relevant literature on the economic consequences of green bonds and the influencing factors of enterprises’ green technology innovation and provides policy suggestions for further improving the analysis of green bonds.

After the outbreak of COVID-19 pandemic, devising an effective reverse logistics supply chain to clean up disaster medical waste is conducive to controlling and containing novel coronavirus transmission. Thus, the focus of this paper concentrates on multi-period multi-type disaster medical waste location-transportation integrated optimization problem with the concern of sustainability, which is formulated as a tri-objective mixed-integer programming model with the goals of maximizing total economic benefits, minimizing total carbon emissions and total potential social risks. Then, a real-world case from Wuhan using CPLEX solver is used to validate the developed model. Results indicate that constructing DMWTTSs with flexible capacity in different periods is encouraged to handle the sharply increasing disaster medical waste. The multi-period decision model outperforms the single-period one in disaster medical waste supply chains because the former has the capability of handling the uncertainties in the future periods. Increasingly, since the increase of budget doesn’t always work well and social resources are limited, the estimation of minimum budget to obtain optimum overall performance is of great importance.

Flexible distributed energy resources, such as energy storage systems (ESSs), are increasingly considered as means for mitigating challenges introduced by the integration of stochastic, variable distributed generation (DG). The optimal operation of a distribution system with ESS can be formulated as a multi-period optimal power flow (MPOPF) problem which involves scheduling of the charging/discharging of the ESS over an extended planning horizon, e.g., for day-ahead operational planning. Although such problems have been the subject of many works in recent years, these works very rarely consider uncertainties in DG, and almost never explicitly consider uncertainties beyond the current operational planning horizon. This article presents a framework of methods and models for accounting for uncertainties due to distributed wind and solar photovoltaic power generation beyond the planning horizon in an AC MPOPF model for distribution systems with ESS. The expected future value of energy stored at the end of the planning horizon is determined as a function of the stochastic DG resource variables and is explicitly included in the objective function. Results for a case study based on a real distribution system in Norway demonstrate the effectiveness of an operational strategy for ESS scheduling accounting for DG uncertainties. The case study compares the application of the framework to wind and solar power generation. Thus, this work also gives insight into how different approaches are appropriate for modeling DG uncertainty for these two forms of variable DG, due to their inherent differences in terms of variability and stochasticity.

IntroductionThe symbiotic relationship between GDP growth and carbon reduction has been a global challenge, particularly for China, where green growth has become an urgent issue. This study explores the role of government data openness as an innovative policy tool to address the structural contradiction between economic growth and emission reduction.MethodsWe treat “government data openness” as a quasi-natural experiment and use panel data from 240 Chinese cities between 2007 and 2022. The empirical analysis employs a difference-in-differences (DID) approach to test the impact of government data openness on carbon intensity. We address endogeneity concerns using two-stage least squares (2SLS) and machine learning methods.ResultsOur findings show that government data openness significantly reduces carbon intensity. The results remain robust after sensitivity tests and the use of alternative methods. Further analysis reveals that the reduction in carbon intensity is primarily driven by green technological innovation and reduced energy consumption intensity.DiscussionThe study highlights that the effect of data openness in reducing carbon intensity is more pronounced in cities located northwest of the Hu Line, cities with better economic development, and those with higher vegetation coverage. Additionally, there is a notable spatial spillover effect in the causal relationship between government data openness and reduced carbon intensity. This research offers important policy insights for promoting green growth in China’s economy.

Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction ( ECER ) fiscal policies on enhancing carbon emission ( CE 1 ) reduction and carbon efficiency ( CE 2 ). It further analyzes the mediating role of Green Innovation ( GI ), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE 2 , a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE 1 reduction and CE 2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE 1 reduction and the improvement of CE 2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals.