This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 81–100 of 359 papers
Chensheng Lv
This study uses a DID model to analyze China's regional carbon trading pilots' impact on corporate ESG performance. Results show that the carbon emission trading mechanism significantly improves ESG ratings through a composite mechanism of …
Haiyan Lan, Guoshuai Sun, Jiao Feng +1
This study develops a dynamic duopoly game model of renewable and thermal power producers under carbon trading and green certificate schemes. Using nonlinear dynamic methods, it finds that very low renewable costs can destabilize the system…
Junhao Ren, Honglin Gao, Lan Zhao +3
This study develops a multi-agent reinforcement learning framework for self-interested microgrids in P2P electricity trading, addressing renewable generation and demand uncertainties. Each microgrid independently bids price and quantity, op…
Jung-Mi Lim, Seongjun Lee, Taeyoung Jin +1
This paper analyzes the impact of changes in allocation and price in the emission trading scheme (ETS) on the electricity market, using a Korean case study. It examines how carbon price fluctuations affect electricity prices and generation …
Z. Gao
This study proposes a hybrid deep learning model integrating LSTM, GCN, and multi-head attention for dynamic carbon pricing optimization in green transportation supply chains. It improves prediction accuracy by 23.7% and cost optimization b…
Ajay Kumar
This study examines the effect of carbon taxes on greenhouse gas emissions in five OECD countries (Denmark, Finland, Sweden, Norway, Slovenia) from 1990 to 2019. Using Pearson correlation and Phillips-Perron unit root tests, it finds that c…
Xiaonan Li, Rongxin Sun, Wenxu Zhang +2
This paper proposes a bilevel optimization model for distributed energy storage configuration that integrates wind-solar uncertainty and a tiered carbon trading scheme. It uses SNGAN and K-means for scenario generation, and an improved Ospr…
Shuhao Li, Yixin Lin, Xiutao Gao +2
This paper proposes a multi-objective optimal scheduling model for integrated energy systems that integrates tiered carbon trading and load-side demand response. A reward-penalty carbon cost model internalizes carbon constraints. Using the …
Lei Qing
This study evaluates alternative ETS designs to support renewable energy development in the Beijing–Tianjin–Hebei region using AHP and fuzzy VIKOR. Environmental effectiveness and technological innovation receive the highest weights. The in…
M. Luomi, Sarah Wahbah
This paper analyzes design choices in emissions trading schemes (ETS) using data from 38 operational systems worldwide. It covers key elements such as scope, cap setting, allocation, and price management, offering best-practice insights for…
Jong-Dae Kwon, E. Kim
This study examines whether greenhouse gas (GHG) emission reductions and emissions trading outcomes under an Emissions Trading Scheme (ETS) are associated with the cost of equity capital. Using a sample of ETS firms from 2015-2020, it finds…
Saurav Kumar, T. Ghosh, S. Banerjee
This study analyzes the macroeconomic dynamics of India's intensity-based Carbon Credit Trading Scheme (CCTS) using an Environmental DSGE model. Distinguishing between thermal and green electricity, it finds that the scheme promotes green e…
Fang Hu, Yubo Li, Thomas Li‐Ping Tang
This paper uses a DSGE model to analyze the interplay of carbon trading mechanisms and government investment policies on carbon emissions and economic growth in China. It finds that independent carbon markets lead to higher emission increas…
Xiaoou Liu
This paper proposes a virtual power plant (VPP) framework with electric heat pump, carbon capture, and shared storage to overcome CHP heat-power coupling in northern China. A leader-follower game optimizes differential pricing considering g…
Ru Wang, Junxiang Li, Ziyin Yang
This paper proposes a multi-energy collaborative pricing mechanism for virtual power plants (VPPs) under carbon trading regulation. It uses a bi-level optimization model to simultaneously maximize user utility and minimize supplier costs, c…
Wei Xu, Lu Zhang, Grace Kouassi
This paper proposes a bi-level optimization framework for coordinating carbon abatement across the multi-echelon power equipment supply chain under carbon quota and trading policies. The upper model minimizes lifecycle emissions, while the …
Chenqian Pan, Chaolin He
Using data from Chinese listed companies in carbon pilot regions (2013-2024), this study examines how carbon trading prices affect the quantity and quality of corporate green technological innovation. A two-way fixed effects model shows tha…
Luping Yu, Yongshan Zhang, Zihao Wang +2
This paper proposes a Bayesian Deep Reinforcement Learning framework for carbon credit pricing that combines probabilistic modeling, uncertainty propagation, and constraint-based reinforcement learning. Experiments across five datasets show…
Xiaohua Song, Tong Liu, Tianyu Wu +1
This paper develops a two-stage robust optimization method for park-level integrated energy systems with tiered carbon trading and bidirectional demand response. Case studies show 50.40% carbon emission reduction and 150 kW peak-valley diff…
Mengxue Ji, Mingsong Sun, S. Deng
This study examines whether China's carbon emissions trading system (CETS) reduces firms' air pollutant emissions. Using firm-level panel data from 1998-2014 and a staggered difference-in-differences design, the authors find that CETS reduc…
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