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Yunnan is rich in renewable energy resources. An understanding of its energy structure and developmental trajectories would assist in enabling the design of suitable decarbonizing pathways and how to fit into the national 30–60 agenda. Drawing from endogenous growth theory and time series analysis, our study employs comparative functions and scenario assessments to predict the changes in the key economic indicators, such as GDP, industrial structure shifts, population, and urbanization rates, during the low-carbon transition. We further show energy structure patterns and intensity trends using regression-modeling and data-fitting methods. Based on our analyses, we project that by 2035, Yunnan's GDP will grow to CNY 5.4761 trillion, with secondary and tertiary industries contributing 88.8%. The population is estimated to grow to 52.08 million with an urbanization rate of 70%. Moreover, fossil fuel energy consumption is forecasted to diminish to 38.7%, and energy consumption intensity is projected to be reduced to 0.38 tons of standard coal per CNY 10,000. If these metrics follow the inherent endogenous growth trend, Yunnan's emissions are forecasted to peak at around 220 million tons of CO2 by 2030. These findings not only provide a data foundation for Yunnan's low-carbon development goals but also illuminate pathways for regions rich in renewable resources to transition towards sustainable growth, emphasizing the harmony between advancement and environmental stewardship. [ABSTRACT FROM AUTHOR]

There is a global move toward being "carbon neutral". Reducing the use of coal to generate power has become an inevitable choice for many countries when transforming their energy structures. Many countries have proposed phasing out coal. China is a major energy producing and consuming country and intends to reach a carbon peak by 2030 and become carbon neutral by 2060. China has repeatedly emphasized coal reduction, but has not explicitly proposed phasing out coal, due to the influence of local governments, coal-related enterprises, and the public. This paper explores whether China could declare a "coal phase-out", and the possible reasons for doing so, by constructing an evolutionary game model with two correlations. MATLAB was used to simulate the model results to determine the effectiveness of the fractal results of the model, and the entropy method was used to calculate the development level of "coal phase-out" related indicators in China and Germany. The results show that: (1) The government can phase out coal only when coal-related enterprises and the public can benefit from reducing coal production and consumption. In addition, these benefits are needed to ensure stable economic and social development without affecting people's daily lives; (2) The development level of relevant indicators of "coal retreat" in China is lower than that in Germany. Based on these results, it is concluded that it is difficult for China to announce a "coal phase-out" at present. Faced with this reality, China should improve the efficiency of coal use, install carbon capture and storage facilities, vigorously develop renewable energy and reduce the share of coal in the energy system. [ABSTRACT FROM AUTHOR]

Developing energy from renewable sources and modernizing the energy system are critical components of China's efforts to combat climate change. Policymakers and authorities have made significant attempts to bring them. However, one of the major impediments to China's energy revolution is financial limitations, which are inextricably linked to the country's economic growth. The present research paper intends to investigate the relationship between economic growth and sustainable financial development on the use of energy from renewable sources in both the short and long run in the context of China. To achieve this, the researchers have utilized the panel data consisting of 10 years from 2011 to 2020. When compared to cross-sectional and time-series data samples, the panel data model offers many benefits. For starters, the panel data includes information on the passage of time and the cross-sectional area. Another benefit of using panel-data models with a larger degree of freedom is that they provide more stable and reliable estimates across short periods across cross-sections. In the case of the short run, there is a positive relationship between economic and financial development and the use of energy from renewable sources in the context of all of China. While in the case of long-term effects, the results indicate the adverse impact of financial development on the use of energy from renewable sources in the western regions of China. These results were deduced using the causality test Granger proposed to determine the path of the causal relationship and the direction of the relationship between the variables. These results indicated that the relationship between economic and financial development in east China was unidirectional, and the nature of the underlying relationship was causal. Meanwhile, in east and west China, economic development in China as a whole has been unidirectionally increasing energy from renewable sources. Our empirical findings suggest many strategies for promoting the growth of energy from renewable sources. [ABSTRACT FROM AUTHOR]

In response to the rising importance of the climate agenda, many countries have restructured their electricity markets to facilitate the utilization of renewable energy. China is an interesting case because it has expanded its utilization of wind and solar energy with unmatched speed. This review starts with an analysis of the 2002 reforms that uncoupled electricity production from transmission. The investigation covers the period leading up to the 2022 proposal, which aimed to build a nationally integrated electricity market. The analysis suggests that a careful alignment of incentives for key market players to produce and consume renewable energy is vital during the process of energy transition. The introduction of feed-in tariffs in 2009 for wind energy, which were subsequently extended to solar energy, stimulated a high growth in installed capacity. However, a high electricity curtailment rate resulted. Since 2018, the Chinese government has resorted to curtailment caps and renewable portfolio standards to increase the utilization of renewable electricity. After the announcement of the "dual carbon goals" in 2020, the Chinese government launched a series of reforms that aimed to nurture growth in the green electricity market and the formation of a nationally integrated electricity market. The removal of interregional trade barriers is a key element of China's current electricity market reforms and will be crucial to determining whether China can achieve its climate goals. [ABSTRACT FROM AUTHOR]

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China's increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China's short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China's short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China's CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China's CO2 emissions. [ABSTRACT FROM AUTHOR]

The decarbonisation targets of the People's Republic of China are ambitious. Their achievement relies on the large-scale deployment of variable renewable energy sources (VRES), such as wind and solar. High penetration of VRES may lead to balancing problems on the grid, which can be compensated by increasing the shifting flexibility capacity of the system by integration with energy storage, e.g., by installing additional electricity storage. Pumped Hydro Storage (PHS) is the most diffused electricity storage technology at the global level and the only fully mature solution for long-term electricity storage. China already has the highest PHS capacity installed worldwide and plans to increase it strongly before 2030. The present study, based on the data from the "Pumped Storage Tracking Tool" of the International Hydropower Association, investigates the potential for technological improvement of the existing and future PHS fleet in China. The aims of adopting advanced PHS solutions allow China to better cope with the task of balancing the VRES production. The potential for adopting advanced PHS solutions is evaluated through five different intervention possibilities (here referred to as scenarios). These scenarios consider revamping part of the operational Pumped Storage Plant (PSP) fleet and redesigning future installations that are already planned. As a result, considering all the major technical and authorisation process constraints, 4.0% (5.2 GW) of the 132 GW fleet expected to be commissioned before 2035 could additionally adopt advanced PHS in a high-potential scenario. Meanwhile in the medium and low potential scenarios, the quota can reach 11.1% (14.6 GW) and 26.2% (34.5 GW), respectively. Furthermore, policy recommendations are elaborated to promote, facilitate, and support the adoption of these advanced PHS solutions. [ABSTRACT FROM AUTHOR]

The Belt and Road investment involves a large number of renewable energy projects, but whether the energy efficiency is linked to the Belt and Road Initiative (BRI) cooperation has yet to be established in the literature. This research attempts to close this gap by looking at how the BRI affects the energy efficiency of participating nations after the fact through a counterfactual analysis. Based on the Difference-in-Difference-in-Difference (DDD) and quantile DID methods for the panel dataset covering 178 nations and areas for the period of 2002 to 2018, we explored the impacts of BRI on energy efficiency vary by different energy efficiency quantiles, resource endowments, and income levels. First, the positive effects of BRI are significant in medium-and high-energy efficiency quantiles but are not significant in low-energy efficiency quantiles for up to 30%. Second, the BRI tends to increase more energy efficiencies in resource-rich BRI countries than those in resource-poor BRI countries. Third, the BRI tends to increase more energy efficiencies in low-income BRI countries than those in high-income BRI countries. The findings can assist BRI countries in improving energy efficiency and sustainable development capacity under the initiative, thus resolving the increasing anxieties in climate change and environmental pollution in this area. [ABSTRACT FROM AUTHOR]