Reconsidering Carbon Permits Auction Mechanism: An Efficient Dynamic Model

China's National Carbon Emissions Trading Market officially launched online trading on July 16, 2021. Based on the externality theory and Coase's Theory of Property, this paper calculates the carbon emission costs of the eight key carbon emission industries under the mechanism of China's carbon trading market. The study shows that, under the current scenario of free allocation of initial quotas, the carbon emission cost of the eight major industries in China accounts for an average of 0.05% of the income. However, when the initial quotas are paid, and the allocation proportion is 100%, the carbon emission cost of each industry increases significantly, accounting for an average of 0.92% of the income. It is concluded that the carbon emission quota control and China's carbon trading market mechanism will increase the carbon emission cost to a certain extent, thus encouraging enterprises to update technology and replace energy to achieve energy conservation and emission reduction. Under the background of the national unified market and given China's “carbon peaking and carbon neutrality” goal, the article puts forward four measures, including expanding the scope of industry participation in carbon trading, changing the amount of free carbon quota, formulating a reasonable total amount of carbon quota, and promoting the integration of local carbon market into the national carbon market, aiming to promote the carbon emission trading market to help China reduce carbon emission.

China's economic growth has entered “new normal,” and the task of reducing carbon emissions has become more onerous. Hence, this study aimed to explore whether China's carbon emissions trading pilot policy stimulated corporate green innovation capabilities. The data pertained to the green patent data of the listed companies in Shanghai and Shenzhen stock exchanges during 2008–2018. Using a difference-in-difference-in-differences (DDD) method, the study took advantage of the variations across regions, across enterprises, and across years and obtained several novel findings. First, the pilot carbon emissions trading policy significantly stimulated the green innovation capabilities of emission control companies in the pilot areas compared with enterprises in nonpilot areas and the nonemission control list. Second, the effect of the policy on the improvement in corporate green innovation capabilities might be driven by the improvement in corporate input factor allocation efficiency and the additional benefits that could be obtained from the carbon trading market. Third, the positive effect of the policy on the green innovation capabilities of state-owned enterprises was more significant. Therefore, the establishment and promotion of a unified national carbon emissions trading market and supporting mechanisms should be accelerated to achieve the balance of stable economic growth and carbon emission task.

The carbon emission rights do not fit well into the framework of existing multi‐item auction mechanisms because of their own unique features. This paper proposes a new auction mechanism which converges to a unique Pareto optimal equilibrium in a finite number of periods. In the proposed auction mechanism, the assignment outcome is Pareto efficient and the carbon emission rights’ resources are efficiently used. For commercial application and theoretical completeness, both discrete and continuous markets—represented by discrete and continuous bid prices, respectively—are examined, and the results show the existence of a Pareto optimal equilibrium under the constraint of individual rationality. With no ties, the Pareto optimal equilibrium can be further proven to be unique.

In recent years, auction theory has been extensively studied and many state-of-art solutions have been proposed aiming at allocating scarce resources (e.g. spectrum resources in wireless communications). Unfortunately, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. On the other hand, performance guarantee, such as social efficiency maximization, is also crucial for auction mechanism design. Therefore, the goal of this work is to design a series of strategyproof and privacy preserving auction mechanisms that maximize the social efficiency. To make the designed auction model more general, we allow the bidders to express their preferences about multiple items, which is often regarded as the multi-unit auction. As computing an optimal allocation in multi-unit auction is NP-hard, we design a set of near optimal allocation mechanisms with privacy preserving separately for: (1) The auction aims at identical multi-items trading; and (2) The auction aims at distinct multi-items trading, which is also known as combinatorial auction. To the best of our knowledge, we are the first to design strategyproof multi-unit auction mechanisms with privacy preserving, which maximize the social efficiency at the same time. The evaluation results corroborate our theoretical analysis, and show that our proposed methods achieve low computation and communication complexity.

Our article investigates the variation of winning bids in slave auctions held in New Orleans from 1804 to 1862. Specifically, we measure the variation in the price of slaves conditional on their geographical origin. Previous work using a regression framework ignored the auction mechanism used to sell slaves. This introduced a bias in the conditional mean of the winning bid because it depended on the number of bidders participating in the auction. Unfortunately, the number of bidders is unobserved by the econometrician.We adopt the standard framework of a symmetric independent private value auction and propose an estimation strategy to attempt to deal with this omitted variable bias. Our estimate of the mean number of bidders doubled from 1804 to 1862. We find the number of bidders had a significant positive effect on the average winning bid. An increase from 20 to 30 bidders in an auction raised the average winning bid by around 10%%. The price variation according to the geographical origin of slaves found in earlier work continues to persist after accounting for the omitted variable. We also find a new result that a considerable premium is paid for slaves originating from New Orleans. However, this price variation disappears once we account for regional productivity differences.

The Kyoto Protocol aims to present three flexible implementation mechanisms in an effort to address climate change, and thus generate a market incorporating carbon emissions as a product: joint implementation ( JI ); a clean development mechanism ( CDM ); and an international emissions trade ( IET ). The carbon market has rapidly developed and expanded to become the new star of global trade: in 2011, trading volume stood at 10.3 billion tCO 2 , with transactions amounting to $176 billion. Some current regional carbon markets include the European Union Emission Trading Scheme ( EU ETS ), the New South Wales ( NSW ), and the Regional Greenhouse Gas Initiative ( RGGI ); China has also tried to establish a domestic carbon trading market and has been piloting a carbon emission trading scheme since 2011. Carbon pricing lies at the heart of carbon market operation; therefore, this article analyzes challenging issues in today's carbon markets, including allowances, energy prices, and special events and also provides policy recommendations for establishing a carbon trading market.

In practice, the rules in most open English auctions require participants to raise bids by a sizeable, discrete amount. Furthermore, some bidders are typically more aggressive in seeking to become the “current bidder” during competitive bidding. Most auction theory, however, has assumed bidders can place any tiny “continuous” bid increase, and recommend as optimal the tiniest possible increase. This article examines how incorporating discrete bidding and bidder aggressiveness affect optimal strategies for an important decision for auction sellers, which is setting the lowest acceptable bid at which to sell the property. We investigate two alternative methods sellers often use to enforce this decision. These are setting an irrevocable reserve before the auction, and covert shilling, where the seller or confederates pose as bona fide bidders and raise bona fide bids, unsuspected by bidders. These optimal strategies interest auction participants, especially sellers who must recognize the bidding rules and bidder aggressiveness they will encounter in actual auctions. We also examine how these strategies change with the auction context, such as the number of bidders, and how they differ from corresponding strategies already identified for continuous bidding. Our model examines open English auctions where bidders have independent, private valuations. We find that discrete bidding does affect these strategies, as does the aggressiveness of the bidder with the highest valuation, relative to the average aggressiveness of all other remaining bidders. We identify the seller's optimal discrete reserve, and show that if the highest valuator is relatively more (less) aggressive, this increases (decreases) from the optimal continuous reserve, and also increases (decreases) as the number of bidders increases. With continuous bidding, by contrast, this reserve is invariant to the number of bidders. As this bidder becomes relatively more aggressive, for a given number of bidders, the optimal discrete reserve increases, while as he or she becomes less aggressive, the seller's expected auction utility increases, which increases the set of auctions where discrete bidding generates higher seller welfare than continuous. We propose a covert shilling model that requires shilling sellers, and any confederates and auctioneers, to outwardly act no differently than with reserves, to avoid detection. We identify cases where the seller optimally shills once the bona fide bidding has stopped, and identify the corresponding optimal point to stop shilling and accept the next bona fide bid, if offered. This stopping point does not depend on where bona fide bidding stops, or aggressiveness, or the number of bidders, or on whether shill bids alternate with bona fide bids or are consecutively entered. We also find that the optimal lowest acceptable bid with shilling can be higher (lower) than that with reserves if the highest valuator is sufficiently unaggressive (aggressive). By comparison, in continuous bidding shilling and reserves yield identical lowest acceptable bids. Sometimes the seller using a shilling strategy optimally should not shill at all, and instead accept the bid where bona fide bidding stops. This can occur when that bid, or the number of bidders, is sufficiently high, or when the highest valuator is as, or less, aggressive than other bidders. Optimal shilling can be as practical to implement as reserves, because it does not require sellers to have any information beyond that needed in a reserve auction. If sellers shill optimally, they can never be worse off compared to using a reserve, and can be better off. Shilling can make bidders worse off, but can also make them better off when the seller using a shilling strategy optimally accepts bids below the optimal reserve. In these latter cases, shilling Pareto dominates reserves, ex ante. We provide numerical examples to illustrate these results. We discuss how our results might be affected if shilling is not covert, or bidders' valuations have a common value component rather than being independent, or by the rules used in many discrete bid Internet auctions.

Research on the efficiency of carbon trading market in China

In the traditional double auction market, the number of achievable tradable pairs are usually limited by the market size. To solve this problem, we propose new double auction mechanisms based on social networks to realize market expansion. In this paper, we examine the double auction market, which consists of a group of sellers and a group of buyers, where the sellers provide the same type of items, and the buyers respectively purchase one from them. In addition, every buyer can invite other potential buyers to enter the market through social networks. The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism. We found that the extended McAfee auction mechanism cannot motivate existing buyers to invite other potential ones, while the traditional Vickrey-Clark-Groves (VCG) mechanism cannot guarantee to break even. To solve these problems, we propose two mechanisms, which are called information network auction (INA) mechanism and double network auction (DNA) mechanism. Both of these mechanisms can encourage old buyers in the market to invite new potential customers to participate. Moreover, INA focuses on achieving more effective allocation and DNA focuses on ensuring a balance between income and expenditure.

Green logistics is an important part of green supply chain management, and its green and low-carbon level greatly affects the level of green supply chain management. In order to achieve China's targets of carbon peaking and carbon neutrality, and to study the emission reduction path of the logistics industry under green supply chain management, this paper uses the scalable STIRPAT model and ridge regression to analyze the influencing factors of carbon emissions in the logistics industry; Taking the relevant data from 2000 to 2020 as an example, combined with the scenario analysis method, this paper conducts an empirical study on the development trend of carbon emissions in China's logistics industry in the future. The results show that: under the low-carbon and enhanced low-carbon scenarios, China's logistics industry can achieve the carbon peak target of 876.6 MtCO2 and 817.02 MtCO2 respectively. Carbon emissions can be reduced to 234.9 MtCO2 by 2060 in the Enhanced Low Carbon Scenario. There is still a certain distance between neutralization. In the meantime, according to the forecast results, this paper, from the perspective of green supply chain management, from the three directions of carbon trading, reverse logistics and emission reduction costs, gives suggestions on the emission reduction path of China's logistics industry under the carbon peaking and carbon neutrality goals: consider early Incorporate the logistics industry into the carbon trading market; increase support and accelerate the development of reverse logistics; increase scientific research investment in clean energy, and promote new energy vehicles. This paper innovatively stands in the perspective of supply chain management, and improves the analysis of influencing factors. For the single industry of logistics industry, the carbon peaking and carbon neutrality goals goal is put in a model, which can better reflect the complete trend and target Provide low-carbon directions and suggestions. In all, these findings contribute to the formulation and implementation of sound policies to help China achieve the carbon peaking and carbon neutrality goals.

With the increasing scale of renewable energy and electric vehicles (EVs), the optimization scheduling of the regional integrated energy system (RIES) is facing new challenges due to the integration of EVs and new energy into the system. To realize the low-carbon economic operation of the RIES, a bi-level optimization scheduling strategy for low-carbon economic operation of the RIES is proposed, which considers EVs' participation and lifecycle carbon emissions, and integrates the electricity market and carbon trading market. Firstly, there are many factors affecting the carbon emissions of EVs and the difficulties in prediction. From the indirect carbon emissions during the use of EVs, the relationship between the carbon emissions of EVs and the charging mode and carbon emission sources is comprehensively analyzed. A carbon emission model of EVs based on the Life Cycle Assessment (LCA) is established in this paper. Secondly, the charging model of EVs based on demand response is combined with the electricity market; The carbon emission model of EVs based on the LCA is combined with the carbon trading market, and a bi-level scheduling strategy based on the flexible interaction between carbon and electricity is proposed. Then the strategy is solved to minimize system economic costs and carbon transaction costs. Finally, simulation example analysis is conducted to demonstrate that the proposed strategy can effectively reduce the total charging costs of electric vehicle users, improve the utilization rate of renewable energy, and reduce carbon dioxide emissions, with good economic and environmental benefits.

China's foreign trade implicit carbon net export country, China's huge trade surplus has brought the huge net exports implicit carbon emissions. There are quite a part of China's large amounts of carbon dioxide emissions is through exports to developed countries bear, only from a unilateral think China bring huge argument for carbon emissions is unfair. For this kind of problem, it should establish and improve the corresponding system and inspection system, and from the Angle of final consumption. It clears the main culprit of a carbon emissions and the necessary assumption of responsibility. Developed countries as an excuse and a carbon tariff on China's export products, an apparent bad intentions with trade protectionism. The international community should see China sacrifice for the development of the world, from a global view of China's carbon emissions.

Global CO2 emissions are on the rise, thus governments are increasingly concerned about carbon emissions. As the trend of China's carbon emissions is also changing, previous studies have looked for factors affecting carbon emissions, including energy and economy, etc. This paper uses the Environmental Kuznets Curve (EKC) theory to have a descriptive statistical analysis of carbon emissions and related economic indicators in mainland China. In the period that follows, China's energy demand potential is huge, but it also faces a severe CO₂ emission reduction challenge. Improving energy efficiency and optimizing energy mix are effective solutions to China's carbon emission problem. China still has a long way to go in industrializing its economy and strengthening the structural transformation of the service sector, characterized by rapid development and huge energy demand. This study will help us to understand the evolution of carbon emissions in China and provide guidance for future carbon emission management and mitigation. Further research and actions to improve energy efficiency, adjust industrial structure and promote clean energy development are important for reducing China's carbon emissions.

In the process of steel production, the use of high temperature and high pressure smelting technology, continuous casting and rolling technology, waste heat recovery technology, can effectively reduce energy consumption and reduce carbon emissions. At present, China's carbon trading only includes power generation industry, but high-intensive industries such as steel have been considered to be included in the carbon market. By the use of energy-saving technology to reduce carbon emissions, steel enterprises participate in the carbon trading market, can realize the value of carbon assets. Based on the advanced energy saving technology in the steel industry, this paper studies the influence of technology diffusion on the participation of steel enterprises in carbon trading. The results show that the stock steel mills choose the technical transformation under the loose quota allocation, and the new technology application of the incremental steel mills under the tightened quota allocation. In the early stage of the operation of the carbon market, loose quotas should be implemented to realize the technical transformation and energy efficiency improvement of the stock steel plants, and the quota can be gradually tightened with the maturity of the market operation.

By introducing the equation of IPCC guidelines (2006), China's total carbon dioxide emissions from 1953-2008 were calculated. Using Johansen's co-integration tests and Error Correction Model (ECM) preceded by various unit root or non-stationarity tests, this paper tested for co-integration between gross emissions and GDP of China. Findings are as follows: (1) the GDP is the Granger causality of carbon emissions, but the emissions is not the Granger causality of GDP. (2) Both in long and short term China's GDP increase would result in the increase of the carbon emissions. (3) The extent of GDP's effect on carbon emissions in short-term is larger than long-run effect. Based on the results, this paper proposed several strategic implications.