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As the global energy transition progresses, green electricity, which is crucial for low-carbon systems, has gained attention. However, the lack of effective market linkages hinders a full understanding of the price transmission effects across green markets. This study uses the Vector Autoregression (VAR) model and Granger causality tests to analyze the price transmission and lag effects between the carbon, green certificate, and China Certified Emission Reduction (CCER) Markets. The findings reveal complex price linkages, offering theoretical insights and policy recommendations for optimizing green electricity markets and environmental rights trading.

As the country with the largest CO 2 emissions in the world, the Chinese government has put forward clear goals of hitting peak carbon emissions by 2030 and carbon neutralization by 2060. Thus, China started piloting carbon emission trading in 2013, and in July 2021 China opened national carbon trading, which is the largest carbon market in the world ( China Launches World, 2021 ). Therefore, it is very important for China to study the role and mechanism of carbon trading at present. Based on the quasi-natural experiment of China’s carbon market pilot, this paper uses panel data of 30 provinces in mainland China from 2008 to 2019 to conduct an empirical study on carbon emission reduction and the economic effects in China’s pilot provinces through a Time-varying Differences-in-Differences method model. The results show that the implementation of a carbon trading policy can significantly inhibit carbon emissions and promote economic growth. At the same time, this paper further analyzes the emission reduction mechanism of the carbon emissions trading policy through the intermediary effect test and finds that the policy mainly realizes carbon emission reduction by changing the energy consumption structure, promoting low-carbon innovation, and upgrading the industrial structure. In addition, innovative research has found the impact of a carbon price signal and marketization on the emission reduction effect of the carbon market. Finally, targeted suggestions are put forward.

At present, the utilization hours of thermal power continue to decline, and renewable energy generation also has a serious socket phenomenon, so it is necessary to guide the construction of power side selection points through price signals, so as to promote the smooth integration of more power generation into the grid. This study focuses on the connection services provided by grid enterprises to power side and demand side, analyzes and summarizes the connection charging practices of different countries, designs a shallow connection cost recovery model according to energy strategy and the characteristics of supply and demand side in our country, and puts forward feasible suggestions for renewable energy generation connection charging, in order to promote the investment and construction of grid and the consumption of renewable energy.

We consider an investor who is dynamically informed about the future evolution of one of the independent Brownian motions driving a stock’s price fluctuations. With linear temporary price impact the resulting optimal investment problem with exponential utility turns out to be not only well posed, but it even allows for a closed-form solution. We describe this solution and the resulting problem value for this stochastic control problem with partial observation by solving its convex-analytic dual problem.

Consumers may decide to modify the profile of their demand from high price periods to low price periods in order to reduce their electricity costs. This optimal load response to electricity prices for demand side management generates different load profiles and provides an opportunity to achieve power loss minimisation in distribution systems. In this study, a new method to achieve power loss minimisation in distribution systems by using a price signal to guide the demand side management is proposed. A fuzzy adaptive particle swarm optimisation is used as a tool for the power loss minimisation study. Simulation results show that the proposed approach is an effective measure to achieve power loss minimisation in distribution systems.

In the context of energy transformation and power market system reform, it is crucial to address the network risks associated with enhancing the integration of the “Energy–Information–Market” paradigm. This necessitates research on multi-energy market trading modes and the corresponding offensive and defensive technologies. This paper proposes a novel approach centered around a node-local Energy Hub (EH) that represents large industrial users with diverse energy demands. To facilitate multi-energy two-way trading, a price-oriented Transactive Energy (TE) market clearing strategy is developed. Building upon this transaction network framework, a data-driven attack strategy targeting the state estimator of the Transmission System Operator (TSO) is introduced and implemented in two stages, encompassing real-time topology estimation and False Data Injection attacks. By leveraging Matrix Transfer Entropy (MTE), the optimal attack target is identified to disrupt the economic stability of the system and the profit of the attacker increases significantly. The proposed attack strategy is validated through simulations conducted on a 30-node system, yielding conclusive evidence of its effectiveness while offering vital insights for system defense.

Auctions are used by local Chinese governments to raise revenue while releasing land for development. Because developers reveal their valuation of parcels through winning, bids for land use rights in subsequent auctions may be influenced by outcomes of earlier government sales. A log linear hedonic regression analysis of more than 900 residential land use rights auctions in Chengdu over eight years reveals that top developers' winning bids signal other developers who adjust their bids in subsequent auctions. Developers appear to believe that the top companies possess superior knowledge through research, experience, or insider information. The results are higher government revenue and rising land prices, which can lead to lessened competition among housing developers, higher house prices, and affordability problems in neighbourhoods where the top developers buy land.

[Display omitted] ► Retail low-price guarantees (LPG) act as signals of low prices. ► Therefore, LPGs associated with low (high) offer prices are genuine (deceptive). ► Consumers with low price confidence respond favorably to genuine or deceptive LPG. ► A genuine LPG does not affect perceptions of more confident consumers. ► However, a deceptive LPG favorably affects their perceptions. Low-price guarantees help buyers make inferences about retailers’ prices. However, researchers are concerned that consumers might be vulnerable to relying on guarantees associated with high market prices. Furthermore, truly low-priced retailers that issue low-price guarantees might be vulnerable to consumers’ discounting of such guarantees. This article experimentally assesses these concerns and finds that the effects of adding a low-price guarantee to a low or high offer price on consumers’ pre-purchase perceptions depend on consumers’ confidence in their product category price knowledge and their decision involvement. The article explores the implications of the findings and provides directions for further research.

This paper compares ad valorem and per unit taxation in the context of price signaling. In the model, a taxation designer chooses between ad valorem and per unit taxation to maximize tax revenues, and a monopoly firm, whose product quality can be either high or low, uses price as a quality signal. The analysis shows that, compared to per unit taxation, ad valorem taxation raises the low-quality firm's mimicking cost and lowers the high-quality firm's signaling cost. This leads to higher transaction volumes and tax revenues.

We characterize a duopoly buffeted by demand and cost shocks. Firms learn about shocks from common observation, private observation, and noisy price signals. Firms internalize how outputs affect a rivals signal, and hence output. We distinguish how the nature of information—public versus private—and of what firms learn about—common versus private values—affect equilibrium outcomes. Firm outputs weigh private information about private values by more than common values. Thus, prices contain more information about private-value shocks.