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In response to global warming, China has formulated the "double carbon" strategic goals of peaking carbon dioxide emissions before 2030 and reaching carbon neutrality before 2060. The problem of rural carbon emissions is often ignored due to underdeveloped industries and services. In this paper, the carbon emission pathways in the rural areas of Guangdong Province are investigated. Since energy consumption is the main source of carbon emissions, the factor analysis was used to analyze the main factors affecting rural household energy consumption and agricultural production energy consumption. Multiple linear regression was conducted to predict the rural energy consumption demand in Guangdong. Furthermore, the current situation and development trend of rural energy supply, demand and consumption structure, and the potential of renewable energy development were considered to predict carbon emissions in the rural areas of Guangdong. Moreover, the carbon emission pathways in the rural areas of Guangdong were discussed under two scenarios: the base scenario and the radical model. [ABSTRACT FROM AUTHOR]

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries. [ABSTRACT FROM AUTHOR]

The power sector plays a significant role in carbon neutrality strategies, and the grid system is a crucial part of the power sector. In particular, with less mitigation potential from technology efficiency, the credit from whole life cycles is critical. This paper describes the investigation of the environmental impacts of various scenarios from the perspective of life cycles. By using the life cycle assessment (LCA) method, various grid systems are examined as a case study, including a traditional, renewable energy, and power storage grid system, as well as a microgrid, in Guangdong. The results highlight the fact that with the systematic improvement of a grid, significant environmental benefits can be achieved. For a grid system, optimization through technology has significant carbon reduction effects even if the power grid structure is not changed. Using renewable energy instead of traditional fuel can reduce the emission of 0.05 kg of CO2-equivalent greenhouse gas per 1 kWh of electricity produced, which is 7.9% of the baseline, and microgrid technology leads to a much greater carbon reduction potential of 23.8% of the baseline. The role of energy storage is undervalued due to the limitations of the data. [ABSTRACT FROM AUTHOR]

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity. [ABSTRACT FROM AUTHOR]

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. The carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China's future carbon quota allocation will gradually transition from free allocation to auction allocation, and the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism, constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model, systematically researches the power generation structure under different auction ratios with the time scale of months, and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions. The results of the study show that after comprehensively comparing the total economic cost, renewable energy development, and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits. [ABSTRACT FROM AUTHOR]

In today's environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China's carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China's carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China's carbon market. [ABSTRACT FROM AUTHOR]

Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China's major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions. [ABSTRACT FROM AUTHOR]