Solving the winner determination problem with discounted bids in transportation auctions

Among studies of the factors that influence carbon emissions and related regulations, economic aggregates, industrial structures, energy structures, population levels, and energy prices have been extensively explored, whereas studies from the perspective of fiscal leverage, particularly of local government investment (LGI), are rare. Of the limited number of studies on the effect of LGI on carbon emissions, most focus on its direct effect. Few studies consider regulatory effects, and there is a lack of emphasis on local areas. Using a cointegration test, a panel data model and clustering analysis based on Chinese data between 2000 and 2013, this study measures the direct role of LGI in carbon dioxide (CO2) emissions reduction. First, overall, within the sample time period, a 1% increase in LGI inhibits carbon emissions by 0.8906% and 0.5851% through its influence on the industrial structure and energy efficiency, respectively, with the industrial structure path playing a greater role than the efficiency path. Second, carbon emissions to some extent exhibit inertia. The previous year’s carbon emissions impact the following year’s carbon emissions by 0.5375%. Thus, if a reduction in carbon emissions in the previous year has a positive effect, then the carbon emissions reduction effect generated by LGI in the following year will be magnified. Third, LGI can effectively reduce carbon emissions, but there are significant regional differences in its impact. For example, in some provinces, such as Sichuan and Anhui, economic growth has not been decoupled from carbon emissions. Fourth, the carbon emissions reduction effect in the 30 provinces and municipalities sampled in this study can be classified into five categories—strong, relatively strong, medium, relatively weak and weak—based on the degree of local governments’ regulation of carbon emissions. The carbon emissions reduction effect of LGI is significant in the western and central regions of China but not in the eastern and northeast regions. This study helps overcome the limitations of previous studies on the regulatory effects of LGI on carbon emissions, and the constructed model may more closely reflect actual economic conditions. Moreover, the current study can benefit countries similar to China that aim to objectively identify the impacts of their LGI on carbon emissions, and such countries can use it as a reference in the formulation of investment policies based on their economic and industrial characteristics.

In this article, we propose two-stage planning models for Electricity-Gas Coupled Integrated Energy System (EGC-IES), in which traditional thermal power plants (TTPPs) are considered to be retrofitted into carbon capture power plants (CCPPs), with power to gas (PtG) coupling CCPPs to the gas system. The sizing and siting of Carbon Capture, Utilization, and Storage (CCUS)/PtG facilities and the operation cost of TTPP/CCPP/gas source/PtG are all considered in the proposed model, including penalty on carbon emissions and revenue of CCUS. With changing policy on climate change and carbon emission regulation, the carbon price, carbon tax, and load uncertainties are also analyzed and considered in the proposed planning model. Stochastic planning and robust planning methods are introduced to verify mutually through economic and carbon indices. Various cases and discussions demonstrate the proposed methods' effectiveness in reducing carbon emissions and increasing CCUS's profit from EGC-IES.

<sec><title>Objective</title> The world is still in a phase of rapid industrialization and urbanization. Excessive carbon emissions has become the primary root cause of various urban or even global environmental problems, further impacting human physiological and psychological health. Cities are the largest sources of carbon emissions and are crucial regions for achieving carbon neutrality goals. Urban blue-green infrastructure (UBGI), comprising natural, semi-natural, or artificial green and blue spaces within cities, is considered as the most important carbon sink space in urban areas and has increasingly attracted widespread attention from researchers. However, there are still many unresolved issues regarding the effectiveness of UBGI in carbon sink enhancement and emission reduction: 1) How is the energy efficiency of carbon sink enhancement and emission reduction measured, and what factors influence it? 2) What are the mechanisms and pathways through which UBGI enhances carbon sink and reduces carbon emission? 3) How can UBGI be regulated to better enhance its effectiveness in carbon sink enhancement and emission reduction? 4) What are the limitations and potential directions for future research? This research aims to address these issues and propose scientifically sound planning strategies for UBGI construction to achieve urban carbon neutrality goals. </sec><sec><title>Methods</title> Through literature synthesis and deduction, this research organizes and analyzes the multi-scale measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction, identifies corresponding influencing factors at each scale, and constructs multi-scale planning strategies for UBGI based on the logical framework of “measurement methods–influencing factors – planning strategies”. </sec><sec><title>Results</title> The research proposes UBGI planning strategies across three spatial scales (site, community and urban area), covering three key aspects: Carbon sequestration and sink enhancement, carbon reduction based on temperature reduction (or preservation), and travel-related carbon reduction. Based on current research gaps and planning needs, five major research topics are further identified. This research provides a detailed analysis of the measurement methods and influencing factors of UBGI’s efficiency in carbon sink enhancement and emission reduction from three perspectives: Carbon sequestration and sink enhancement, carbon reduction based on temperature reduction (or preservation), and travel-related carbon reduction. The research finds significant differences in the measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction efficiency across different scales. Contradictory results may occur at different scales, and large-scale research often lacks characterization of internal features, leading to unclear mechanisms of influencing factors and obstructing practical planning. Based on the interpretation of UBGI’s mechanisms for carbon sink enhancement and emission reduction at different scales, this research formulates UBGI planning strategies across three spatial scales (site, community, and urban area). These strategies include: 1) At the site scale, for carbon sequestration and sink enhancement – carbon sink at the source, land balance, and ecological design; for emission reduction – symbiosis with buildings and integration into daily life. 2) At the community scale, for carbon sequestration – overall balance of revenue and expenditure, precise positioning, and proper interconnection of the carbon chain; for emission reduction – incorporation of cool islands and co-construction. 3) At the urban area scale, for carbon sequestration – enhancement of ecological space management and establishment of a carbon-safe pattern; for emission reduction – demand-based layout and organic dispersion. Finally, the research proposes five major research topics for the planning of UBGI’s carbon sink enhancement and emission reduction: How to construct unified measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction across scales? How to measure UBGI’s efficiency in carbon reduction based on temperature reduction (or preservation) at the site scale? How to integrate the pathways of carbon sink enhancement and emission reduction for a life cycle assessment of UBGI? How to balance UBGI’s carbon sink enhancement and emission reduction with other functions to achieve the optimal layout for comprehensive benefits? How to achieve urban “carbon justice” through UBGI? </sec><sec><title>Conclusion</title> The carbon sink pathway of the strategy framework requires “carbon sink at the source – precise positioning – safe pattern”, and the emission reduction pathway requires “symbiotic integration – co-construction and sharing – organic dispersion”. The key trade-offs between these two pathways at three spatial scales may provide theoretical support and practical guidance for UBGI construction and management. The five major research topics mentioned above may offer valuable assistance for UBGI construction and future research. </sec>

Using the panel data of Shanghai and Shenzhen A-share listed companies spanning from 2007 to 2021, we explore the impact of the synergism of carbon emission trading policy and carbon tax policy on the carbon emission level of enterprises through the difference-in-difference model, intermediary effect model, regulatory effect model. The research results show that: (1) Carbon emission trading and carbon tax both promote enterprises’ carbon emission reduction. (2) There are three paths for carbon emission trading and carbon tax policies to encourage enterprises’ carbon emission reduction actions synergistically: improving enterprises’ green technology innovation level, optimising energy structure, and accelerating industrial structure optimisation. (3) Mediated regulatory effect results show that the greater the company’s government support and R&D investment, the greater the synergism between carbon trading and carbon tax policies to drive green technology innovation and the better the carbon emission reduction effect of the company. (4) The synergism between carbon trading and carbon tax policies positively fosters public ownership companies’ carbon emissions reductions and heavily polluted industry companies.

In Combinatorial Auctions, multiple goods (items) are available for auction simultaneously, and bidders bid for combinations of goods called bundles. In the single unit combinatorial auction problem, there is only single unit of each item present and the items are considered to be indivisible. This forms the supply side constraint resulting in a need by the seller to decide on which bundles to allocate for maximizing the revenue. Determining these winning bundles is an NP-complete problem and is known by the name of Winner Determination Problem (WDP) in literature. Combinatorial auction mechanism has a wide range of practical applications such as allocation of railroads, auction of adjacent pieces of real estate, auction of airport landing slots, distributed job shop scheduling etc. The optimal heuristic search algorithms like CASS and CABOB proposed for solving the WDP take a lot of CPU time for solving large problem instances. Though these algorithms give optimal solutions, they would be useful only in scenarios where time is not at all a constraint, and the bidding is a one time affair. This poses a limitation in conducting combinatorial auctions with multiple rounds of bidding and hence hinders the widespread use of combinatorial auctions on the patterns of traditional English auction and internet auctions. Thus faster solution of WDP holds the key to widespread use and popularity of combinatorial auctions. This paper attempts to address the above issue, and proposes a simple local search technique LSWDP (Local Search for Winner Determination Problem) that runs very fast and provides solutions quite close to optimal for a number of applications. It also outputs optimal solutions in many instances taking only a fraction of the CPU time taken by CASS. In addition LSWDP was found to outperform the anytime algorithm CASS given a time cutoff. This paper also presents an enhancement called LSCN (Local Search using Complimentary Neighborhood) that provides still better solutions in terms of close to optimal behavior and achieves a much better strike rate in hitting optimal solutions. LSCN runs slower than LSWDP but still takes only a fraction of the CPU time taken by CASS thus encouraging the use of local search for combinatorial auctions. Finally, an extension to multi-partition search has been proposed and analyzed.

Exact Algorithms for the Matrix Bid Auction

The distributionally robust newsvendor problem with dual sourcing under carbon tax and cap-and-trade regulations

To achieve the goal of stabilizing carbon dioxide at acceptable levels under the Paris agreement, it is essential to control carbon emissions by implementing regulatory interventions. Carbon tax and emission trading system (ETS) are two effective tools to regulate carbon emissions. Various studies have investigated both methods, yet few have considered the impact of these two policies on the electricity market and social welfare. The purpose of this paper is to fill in the gap, namely to quantify the influence of the carbon tax and ETS using the baseline-and-credit method on the electricity market. In addition, an optimal hybrid policy that combines both carbon tax and ETS is developed. Our case studies demonstrate that carbon tax, ETS, and the hybrid carbon policy can achieve the goal of reducing carbon emission and increasing social welfare. For hybrid policy, the carbon tax rate and the emission marginal cost of allowance in ETS are optimized properly to maximize social welfare.

Combinatorial auction, where bidders can bid on bundles of items, has been the subject of increasing interest in recent years. Although much research work has been conducted on combinatorial auctions, most has been focusing on the winner determination problem. A largely unexplored area of research in combinatorial auctions is the design of bidding strategies, in particular, those that can be used in open and dynamic markets in which market situation is generally constantly changing. Obviously, a good bidding strategy to be used in such realistic markets cannot be obtained by analytical methods, which require all market information be known before a solution can possibly be found. Machine learning based approaches are not completely appropriate, either, as an optimal strategy learned from one market generally no longer perform well when the market situation changes. In this paper, we propose a new adaptive bidding strategy for combinatorial auction-based resource allocation problem in such dynamic markets. A bidder adopting this strategy constantly perceives the market situation, and adaptively reviews and adjusts his bid determination, thus responding to the dynamic market in a timely manner. Experiment results show that agents adopting this adaptive bidding strategy perform very well, even without any prior knowledge about the market.

Auctions, and combinatorial auctions (CAs), have been successfully employed to solve coordination problems in a wide range of application domains. However, the scale of CAs that can be optimally solved is small because of the complexity of the winner determination problem (WDP), namely of finding the bids that maximise the auctioneer’s revenue. A way of approximating the solution of a WDP is to solve its linear programming relaxation. The recently proposed Alternate Direction Dual Decomposition algorithm (AD 3) has been shown to efficiently solve large-scale LP relaxations. Hence, in this paper we show how to encode the WDP so that it can be approximated by means of AD 3. Moreover, we present PAR-AD 3, the first parallel implementation of AD 3. PAR-AD 3 shows to be up to 12.4 times faster than CPLEX in a single-thread execution, and up to 23 times faster than parallel CPLEX in an 8-core architecture. Therefore PAR-AD 3 becomes the algorithm of choice to solve large-scale WDP LP relaxations for hard instances. Furthermore, PAR-AD 3 has potential when considering large-scale coordination problems that must be solved as optimisation problems.

Order acceptance and scheduling (OAS) problems are realistic for enterprises. They have to select the appropriate orders according to their capacity limitations and profit consideration, and then complete these orders by their due dates or no later than their deadlines. OAS problems have attracted significant attention in supply chain management. However, there is an issue that has not been studied well. To our best knowledge, no prior research examines the carbon emission cost and the time-of-use electricity cost in the OAS problems. The carbon emission during the on-peak hours is lower than the one in mid-peak and off-peak hours. However, the electricity cost during the on-peak hours is higher than the one during mid-peak and off-peak hours when time-of-use electricity (TOU) tariff is used. There is a trade-off between sustainable scheduling and the electricity cost. To calculate the objective value, a carbon tax and carbon dioxide emission factor are included when we evaluate the carbon emission cost. The objective function is to maximize the total revenue of the accepted orders and then subtract the carbon emission cost and the electricity cost under different time intervals on a single machine with sequence-dependent setup times and release date. This research proposes a mixed-integer linear programming model (MILP) and a relaxation method of MILP model to solve this problem. It is of importance because the OAS problems are practical in industry. This paper could attract the attention of academic researchers as well as the practitioners.

Carbon verification agencies and power enterprises play a crucial role in the process of reducing carbon emissions. Under government regulation, this paper explores the low-carbon behavior of carbon verification agencies and power enterprises, considering factors such as rewards and penalties, reputation, collusion, and costs. We first constructed a carbon emission reduction game model using evolutionary game theory and replicated dynamic equations to analyze the interactions between carbon verification agencies and power enterprises under government oversight. Subsequently, this study used theoretical derivation and numerical simulation to investigate the model’s evolution and the influence of various factors on the system’s evolution results. It is found that, firstly, the carbon emission reduction game between the carbon verification agency and the power enterprises will eventually be stabilized in two states (authentic verification and carbon emission reduction) and (fraudulent verification and no carbon emission reduction), and the specific stabilization of which state is closely related to the selection of the initial values of the parameters. Secondly, within a certain range, increasing the government’s rewards and penalties, increasing the reputation loss of carbon verification agencies and power enterprises, reducing the benefits of collusion between two parties, reducing the cost of low carbon disclosure and emission reduction of power enterprises will help the construction of a cooperative pattern of low carbon emission reduction and authentic supervision of carbon verification agencies.

Optimal decisions for competitive manufacturers under carbon tax and cap-and-trade policies

This special issue includes four articles that were presented in the 15th Workshop on Information Technology and Systems (WITS’05), held in December 10–11, 2005, Las Vegas, Nevada. Since its first meeting in 1991 in MIT, WITS has become a premier annual forum for discussion and interaction among scholars with research interests in the cutting edge issues of information technology and systems. WITS’05 addressed many important research issues as the corporate world evolves toward service-oriented computing that can support business on demand, enabled by the emergence of web services, agent technologies, wireless computing, e-commerce standards, P2P technologies, the business web, among other technologies. The four selected articles have been extended substantially and went through two rounds of reviews. Collectively, these four articles explore new patterns and mechanisms that extend the new boundaries of information technologies and systems. It is our pleasure to make these four fine articles available to the community through the journal of Information Technology and Management. We thank all authors and reviewers for their diligent work that made this special issue possible. Next, we give a brief overview of the four papers in terms of their main discoveries and important contributions. The first article, ‘‘Combining probability models and Web mining models: A framework for proper name transliteration’’ by Zhou, Huang, and Chen combines probability models and web mining models to develop a framework for proper name transliteration. The authors argue that web pages are created today in almost every popular language. However, language boundaries prevent information sharing and discovery across countries. Their paper studies transliteration (referred to that proper names are translated phonetically) issue on the Web. Previous transliteration models can be categorized into four approaches: a rule-based approach, a machine learning approach, a statistical approach, and a web mining approach. This paper proposes a generic proper name transliteration framework which improves the traditional statistical approach by incorporating an enhanced Hidden Markov Model and a web mining model. Evaluation of the proposed framework is very promising as it boosted the performance by 79.05%. Singh and Sen in their article ‘‘Exploring local search in winner determination problem,’’ report their research in developing a new combinatorial auction mechanism for a wide range of practical applications such as allocation of railroads, auction of adjacent pieces of real estate, auction of airport landing slots, and distributed job shop scheduling. Determining the winning bundles in combinatorial auctions is an NP-complete problem. The inefficiency problem in existing approaches to combinatorial auction poses a limitation in conducting combinatorial auctions with multiple rounds of bidding and hinders the widespread use of combinatorial auctions on the Internet. This paper proposes a local search algorithm for combinatorial auctions, which takes only fraction of the CPU time taken by T. Han College of Business, University of Nevada, NV, Las Vegas 89154, USA

Environmental sustainability is receiving growing global attention, making the development of low-carbon and green transportation increasingly important. Low-carbon policies offer significant advantages in incentivizing energy conservation and reducing emissions in the transportation sector; however, it is vital to consider the impacts of regional differences on the implementation effect of low-carbon policies. This paper explores multimodal transportation route optimization under a carbon tax policy. First, a bi-objective route optimization model is constructed, with the goal of minimizing total transportation cost and time, while accounting for uncertain demand, fixed departure schedules, and regional differences. Trapezoidal fuzzy numbers are used to represent uncertain demand, and a fuzzy adaptive non-dominated sorting genetic algorithm is designed to solve the bi-objective optimization model. The algorithm is then tested on differently sized networks and on real-world transportation networks in eastern and western China to validate its effectiveness and to assess the impacts of regional differences. The experimental results show the following. (1) When considering transportation tasks at different network scales, the proposed fuzzy adaptive non-dominated sorting genetic algorithm outperforms the NSGA-II algorithm, achieving minimum differences in percentages of cost and time of 9.25% and 7.72%, respectively. (2) For transportation tasks assessed using real-world networks in eastern and western China, an increase in the carbon tax rate significantly affects carbon emissions, costs, and time. The degree of carbon emission reduction varies depending on the development of the regional transportation network. In the more developed eastern region, carbon emissions are reduced by up to 44.17% as the carbon tax rate increases. In the less developed western region, the maximum reduction in carbon emissions is 14.37%. The carbon tax policy has a more limited impact in the western region compared to the eastern one. Therefore, formulating differentiated carbon tax policies based on local conditions is an effective way to maximize the economic and environmental benefits of multimodal transportation.