The development and utilization of wind energy has alleviated the problems of energy shortage and environmental pollution; however, it caused many negative impacts due to suboptimal site selections. This study proposes an innovative method integrating Geographic Information System (GIS), fuzzy Analytic Hierarchy Process (FAHP), and fuzzy VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) for site selection of wind farms in the Wafangdian region, China. The uncertainties caused by subjective judgments of the stakeholders were tackled by the FAHP method firstly, where weight values of six criteria were identified. Next, the fuzzy VIKOR method and GIS tool were used to generate the Qi value of each location for ranking their appropriate degrees for wind energy development. The results demonstrated that the middle and upper parts of the studied area are suitable for the exploitation of wind energy, while the central and eastern areas are unfavorable. The influences exerted by various weight combinations and climate change on a site suitability assessment were examined. The resulting comparison with existing wind farms reflected the practicability and reliability of the proposed method; the estimation of climate change impacts on site selection provided the suggestion and support of a long-term plan for wind power development, and even the energy structure adjustment scheme adapted to climate change.

With the purpose of achieving carbon emission reduction targets, the wind power industry has developed rapidly in recent years. Wind power is greatly affected by climate change, and the increase or decrease of wind speed directly affects wind energy production. Based on the numerical simulation results from a high-resolution (~ 25 km) regional climate model PRECIS, we analyze the changes of future wind speed and wind power potential in the "Three North" (TN) region in China. Firstly, we verify whether the PRECIS can capture the current spatiotemporal patterns in simulating the wind speed compared with observation (CN05.1). The results show PRECIS has a good ability in reproducing the spatiotemporal patterns of wind speed in the eastern part of the TN region, but still has great uncertainty in the northwest. In the future, the projected wind power density in the TN region will increase by about 0.7% in the middle of the 21^st century, but will drop significantly in the end of the century (about −3.32%). Furthermore, Wind power density will increase significantly in winter. However, the wind speed in spring and summer will generally decrease. It is predicted that most of the Northwest (NW) and North (N) will have strong inter-annual variability in the middle of this century, and will be more stable at the end of this century. It should be noted that the potential of wind energy in Northeast (NE) will grow steadily in this century, which will have certain guiding significance for future wind power planning in this region.

To ensure a smooth transition towards peak carbon emissions and carbon neutrality, one key strategy is to promote a low-carbon transition in the energy sector by facilitating the coordinated development of the electricity market, carbon market, and other markets. Currently, China’s national carbon market primarily focuses on the power generation industry. High-energy-consuming industries such as the steel industry not only participate in the electricity market but also play a significant role in China’s future carbon market. Despite existing research on market mechanisms, there remains a significant research gap in understanding how steel enterprises adjust their trading behaviors to optimize costs in multi-market coupling contexts. This study employs a system dynamics approach to model the trading interconnection between electricity trading (ET), carbon emission trading (CET), and tradable green certificates (TGC). Within this multi-market system, thermal power enterprises and renewable generators serve as suppliers of carbon allowances and green certificates, respectively, while steel companies must meet both carbon emission constraints and renewable energy consumption obligations. The results show that companies can reduce future market transaction costs by increasing the proportion of medium to long-term electricity contracts and the purchase ratio of green electricity. Additionally, a lower proportion of free quotas leads to increased costs in the carbon market transactions in later stages. Therefore, it is beneficial for steel companies to conduct cost analyses of their participation in multivariate market transactions in the long run and adapt to market changes in advance and formulate rational market trading strategies.

In recent years, with the increase of annual average temperature and the decrease of annual precipitation in Beijing, the fragility of Beijing’s energy system has become more and more prominent, especially the balance of electricity supply and demand in extreme weather. In the context of unstable supply of new and renewable energies, it is imperative to strengthen the ability of the energy system to adapt to climate change. This study first simulated climate change in Beijing based on regional climate data. At the same time, the Statistical Program for Social Sciences was used to perform multiple linear regression analysis on Beijing’s future power demand and to analyze the impact of climate change on electricity supply in both the RCP4.5 and RCP8.5 (representative concentration pathway 4.5 and 8.5) scenarios. Based on the analysis of the impact of climate change on energy supply, a multi-objective optimization model for new and renewable energy structure adjustment combined with climate change was proposed. The model was then used to predict the optimal power generation of the five energy types under different conditions in 2020. Through comparison of the results, it was found that the development amount and development ratio of various energy forms underwent certain changes. In the case of climate change, the priority development order of new and renewable energies in Beijing was: external electricity > other renewable energy > solar energy > wind energy > biomass energy. The energy structure adjustment program in the context of climate change will contribute to accelerating the development and utilization of new and renewable energies, alleviating the imbalance between power supply and demand and improving energy security.

As global warming intensifies and the frequency of extreme weather events rises, posing a major threat to the world’s economy and sustainable development, accurate forecasting of future extreme events is of great significance to mankind’s response to extreme weather events and to the sustainable development of society. Global Climate Models (GCMs) have limitations in their applicability at regional scales due to their coarse resolution. Utilizing dynamical downscaling methods based on regional climate models (RCMs) is an essential approach to obtaining high-resolution climate simulation information in future. This study represents an attempt to extend the use of the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model by employing the BCC-CSM2-MR model from the Beijing Climate Center to drive it, conducting downscaling experiments over China at a spatial resolution of 0.22° (25 km). The simulation and prediction of daily maximum and minimum temperatures across the China region are conducted, marking a significant effort to expand the usage of PRECIS with data from alternative GCMs. The results indicate that PRECIS performs well in simulating the daily maximum and minimum temperatures over the China region, accurately capturing their spatial distribution and demonstrating notable simulation capabilities for both cold and warm regions. In the annual cycle, the simulation performance of PRECIS is superior to its driving GCM, particularly during cold months (i.e., December and from January to May). Regarding future changes, the daily extreme temperatures in most regions are projected to increase gradually over time. In the early 21st century, the warming magnitude is approximately 1.5 °C, reaching around 3 °C by the end of the century, with even higher warming magnitudes exceeding 4.5 °C under the SSP585 scenario. Northern regions will experience greater warming magnitudes than southern regions, suggesting faster increases in extreme temperatures in higher latitudes. This paper provides forecasts of extreme temperatures in China, which will be useful for studying extreme events and for the government to make decisions in response to extreme events.

In the context of China’s transition from “dual control of energy consumption” to “dual control of carbon emissions,” understanding the synergistic mechanisms among carbon emission trading (CET), energy use rights trading (EURT), and electricity markets is critical for achieving the nation’s dual carbon goals. This study develops a system dynamics (SD) model to examine the coupled interactions within this “carbon–electricity–energy” ternary market system, focusing on thermal power enterprises as the primary analytical subject. The model reveals that the ternary market framework drives energy conservation and emission reduction through three key mechanisms: price signal transmission, dual regulatory constraints, and mutual quota recognition. These mechanisms propagate low-carbon incentives throughout the industrial chain by transmitting cost signals to end-users via electricity prices. Compared to binary market structures, the ternary framework achieves superior outcomes, it facilitates higher renewable energy consumption, maintains more stable price levels, enhances market liquidity for both carbon and energy rights, and improves resource allocation efficiency alongside environmental–economic performance. However, the simulation also exposes critical inefficiencies under the current “dual control of energy consumption” regime. The parallel operation of EURT and CET markets creates functional overlap and duplicated compliance burdens. This redundancy increases enterprise costs without commensurate environmental gains, validating the necessity of transitioning to carbon-focused dual control. Further analysis demonstrates that a mutual recognition mechanism between carbon and energy rights effectively alleviates dual compliance pressures and improves enterprise profitability. Optimal market performance emerges when the recognition ratio is appropriately calibrated. Additionally, gradually increasing the share of auctioned quotas while maintaining appropriate levels of free allowances can drive emission reductions without compromising enterprise profitability. This research provides both theoretical foundations and practical policy recommendations for building an efficient multi-market coordination mechanism, facilitating the policy transition, and advancing low-carbon transformation in China’s power sector.