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With the development of energy technology, hydrogen is gradually becoming widely used. China is also rapidly developing its own hydrogen energy industry in an effort to accomplish the "dual carbon target'. Hydrogen from methane is one of the mainstream sources, of which hydrogen from methane cracking is the decomposition of methane into hydrogen and solid carbon, with the solid carbon also having a high economic value. In particular, this technology does not emit carbon dioxide, which is very friendly to the environment, but it cannot avoid larger energy consumption. In contrast, hydrogen production from methane cracking driven by solar energy not only combines the characteristics of no CO 2 emission, but also greatly reduces the consumption of primary energy, which is the future development trend of hydrogen production from methane. This paper provides a brief overview of the various technological pathways for methane to hydrogen production in the context of China's actual development, focusing on the current status of the development and application of solar methane cracking to hydrogen production technologies at home and abroad. Among them, this paper mainly discusses the characteristics of solar reactors of different sizes and structures, and summarizes the results of related optimization studies. Meanwhile, the application of catalysts in solar reactors and the problem of carbon deposition are analyzed, and the practical application of solar methane cracking hydrogen production system is outlined. Finally, the future development of solar methane cracking hydrogen production technology is prospected from the national situation of China. [Display omitted] • Solar methane cracking for hydrogen can reduce emissions and energy consumption. • The current state of research on solar reactors is summarized. • Solar methane cracking requires higher performing catalysts. • Providing insights into the future development of solar methane cracking in China. [ABSTRACT FROM AUTHOR]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]

China, a country with a long-standing agricultural legacy, is increasingly prioritizing the reduction of CO2 emissions from its agricultural sector. Initially, the carbon emission sources within the agricultural sector are classified into two categories: direct and indirect emissions. Using this classification, the study calculates the generalized agricultural carbon emissions (GACEs) of 30 provinces in China between 2011 and 2020. To further understand the factors influencing GACEs, the paper employs the logarithmic mean Divisia index method and Tapio decoupling index to analyze seven key factors. These factors include carbon emission intensity, energy consumption of generalized agriculture, and economic benefit level of energy consumption. By comparing the impact and changes of GACEs during the 12th and 13th five-year plan periods, the study reveals valuable insights. The findings suggest that carbon emission intensity plays a crucial role in suppressing GACEs, while the level of economic development acts as a catalyst for their increase. By effectively managing these influencing factors, the paper proposes that the increase in GACEs can be effectively suppressed, and the achievement of agricultural CO2 reduction goals can be expedited. [ABSTRACT FROM AUTHOR]

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development. [ABSTRACT FROM AUTHOR]

Recycling carbon tax revenue is a significant part of the design of a nation's carbon tax system. The impact of different recycling principles on the socio-economy and carbon emission reduction varies greatly. Under the background of China's goal of reaching peak carbon emissions, this paper designed three carbon tax recycling principles based on the goal of Nationally Determined Contributions (NDCs), including recycling principles based on output ratio, export ratio, and carbon intensity. A recursively dynamic CGE model was developed in this study to evaluate the economic effects of these three carbon tax recycling principles based on tax neutrality under China's goal of reaching peak carbon emissions. Carbon tax recycling measures could mitigate the economic losses caused by carbon tax, and the results could be greatly affected according to different recycling principles. The results showed that: 1 The recycling principle based on output ratio could reduce economic losses and improve households' social welfare. 2 China's carbon intensity in 2030 could be reduced by more than 65% compared with that in 2005, and the proportion of non-fossil energy in primary energy consumption could reach 25% using the recycling principle based on carbon intensity, but the economic loss mitigation effect of the recycling measures based on this principle was small. 3 Adopting the recycling principle based on export ratio would narrow the competitive gap between China and foreign countries, and the exports of high-carbon-intensity goods would not increase under this principle. 4 According to principles based on output ratio and export ratio, the NDC goal of non-fossil energy accounting for about 25% of primary energy consumption by 2030 could not be achieved, and only the NDC goal of reducing carbon emissions per unit of GDP by more than 65% compared with that of 2005 could be achieved. In brief, as an essential policy tool to achieve China's goal of reaching peak carbon emissions, the carbon tax should be implemented as soon as possible. At the same time, according to China's national conditions, a targeted choice of carbon tax recycling principle is needed. The role of the carbon tax in the process of achieving the goal of Nationally Determined Contributions should be fully utilized. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

With the completion of the poverty alleviation task in China, the Chinese economy has made great breakthroughs. Meanwhile, the implementation of Chinese "dual carbon" goal has entered an important window period of both strategic opportunities and difficulties. The emission of carbon dioxide (CO2) leads to the greenhouse effect, which affects the sustainable development of society. Therefore, this paper proposes an improved analytic hierarchy process (AHP) with Slacks-based measure integrating data envelopment analysis (SBMDEA) (AHP-SBMDEA) based efficiency evaluation model for different provincial-level administrative regions in China, which is used to analyze and optimize the Chinese energy structure of different provincial-level administrative regions. With coal, gasoline, petroleum, fuel oil, diesel, crude oil and natural gas as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, after linear weighted calculation of the results of the AHP-SP model and the SBMDEA model, the energy efficiency evaluation model of different provincial administrative regions in China is established. The results show that there is a large difference in efficiency between Chinese northern and southern regions, and the overall efficiency of southern regions is higher than that of northern regions. In addition, it is easier to find out the optimal energy efficiency benchmark, which can provide suggestions and help to guide the energy consumption input structure improvement of other inefficient provincial-level administrative regions. Furthermore, the energy structure can also be adjusted through the redundant variable calculated by the SBMDEA model. For example, Sichuan Province can reduce the consumption of the coal, the gasoline, the diesel, the fuel oil, the crude oil and the natural gas by 5269.95 × 10000 tons (WT), 513.01 WT, 59.05 WT, 34.37 WT, 4,73.33 WT and 130.37 billion cubic meters (BCM), respectively. Its CO2 emissions can be reduced by 1925.4 WT, and raise the efficiency of energy utilization in the province. [Display omitted] • Novel economy and CO 2 emissions evolution model based on a AHP-SBMDEA is proposed. • The proposed model can identify the optimal energy efficiency benchmark. • GDP per capita can be increased and CO 2 emissions can be decreased. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

• Carbon emissions from energy consumption of 282 prefecture-level cities in China were calculated. • A new method for calculating carbon emission factors for electricity is proposed. • Extending the Atkinson Index to the application of Carbon Fairness. • Measured the inequality and fairness indicators of carbon emissions from energy consumption at the prefecture-level city level in China. This paper focuses on the conceptualization and quantification of carbon emission inequality and fairness from energy consumption. In order to more accurately measure the carbon emissions generated by electricity consumption in each city, we firstly propose a new method for calculating the carbon emission factor for electricity, and calculates the carbon emissions from energy consumption in 282 prefecture-level cities in China from 2003 to 2019. Through the application of the Theil-Kaya decomposition mechanism and the improved Atkinson index, we conducted an in-depth study of the carbon emission inequality and fairness indicators from energy consumption. The results show that (1) Although China's overall inequality has declined, it remains significant in the western region, with its inequality indicator fluctuating from 0.548 in 2003 to 0.683 in 2019.(2) The decomposition of the drivers of inequality indicators shows that the contribution of GDP per capita to inequality is increasingly being replaced by energy intensity and the energy carbon emission factor, with energy intensity emerging as the main driver, contributing more than 40% to inequality in 2019, both overall and in the three regions.(3) The results for the fairness indicator, on the other hand, show a certain degree of divergence. Further Dynamic Time Warping fitting analysis shows that regardless of how the public's sensitivity to carbon emissions varies, the change trends of the fairness and inequality indicators for carbon emissions in the eastern region show a high degree of convergence, even in the two most extreme cases. The fit is poorest in the western region, but improves significantly if public's sensitivity to carbon emissions increases. (4)To sum up, some policies that are consistent with the context of the times, such as the East-West regional counterparts and the construction of a new type of power system, are worthy of priority consideration by the Government. [ABSTRACT FROM AUTHOR]