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Harmonizing the cleanliness and economics of energy is an effective means of integrating economic growth with carbon peaking goals, and realizing sustainable development in China. However, due to the existence of the energy trilemma, a worthwhile research question is how to fulfill the carbon peaking commitment while guaranteeing China's stable national development. Based on this, this paper not only discusses the nonlinear relationship between clean energy consumption transition and economic growth through threshold regression, but also simulates several scenarios of energy transition dynamics with different growth rates by using Oracle Crystal Ball software to forecast the future trend of China's carbon emissions up to 2030, aiming to find a transition scenario that can make the peak of carbon emissions appear without significant economic burden, so as to put forward countermeasures and suggestions to avoid falling into the energy trilemma in the process of fulfilling the commitment of carbon peaking. This paper finds that: firstly, there is an inverted U-shaped non-linear relationship between clean energy consumption transition and economic growth, with the inflection point of the inverted U-shape being 0.595; and secondly, only when energy transition exceeds 0.535 in 2030, the peak of carbon emissions (about 12,922.28 Mt) will occur. In summary, this paper argues that China should steadily advance energy transition, avoiding either too fast or too slow a pace (bring clean energy consumption transition to within the 0.535–0.595 range by 2030), in order to secure economic growth while honoring its commitment to peak carbon. Therefore, China should prudently design a coal exit strategy to avoid falling into the energy trilemma when promoting energy transition. Moreover, if China wants to further fulfill its commitment to carbon neutrality by 2060, it needs to continue to deepen the construction of green financial markets in order to better balance the relationship between energy cleanliness and economy. [ABSTRACT FROM AUTHOR]

Energy quota trading is critical to both energy conservation and the achievement of dual-carbon goals. However, little remains to be done on total factor productivity and economic potential for Chinese industry. The purpose of this paper is to counterfactually model and explore the effects of energy quota trading on total factor productivity and economic potential in industrial sector. We propose a command-controlled DEA model and an energy quota trading DEA model through non-parametric linear programming based on two policy strategies of energy consumption control. We then assess total factor productivity and its growth sources for 35 two-digit industrial sector in China, as well as examine economic potential and optimal path forecasts for carbon peaking. The results show that total factor productivity and economic potential for energy quota trading are higher than those for command-controlled energy policy. Energy-saving effect can be achieved for energy quota trading because the total energy input is lower. Additionally, the sources of productivity growth differ in timing. The average economic potential and total factor productivity of different industries differ due to the heterogeneity of the production conditions and scale of development of individual market players. Moreover, the catch-up emission reduction path with an increase of 8% in output and 4.76% in energy consumption is the optimal carbon peaking path under energy quota trading. Our findings shed valuable light on China's efforts to expand energy quota trading for high-quality development in the context of carbon peaking. • Propose a command-controlled DEA model and an energy quota trading DEA model. • Assess TFP and its growth sources for industrial sector in China. • Compare the impact of different energy policies on economic potential and TFP. • Estimate the energy saving and emission reduction potential of energy quota trading. • Examine the optimal energy-saving and emission-reduction path forecasts for carbon peaking.. [ABSTRACT FROM AUTHOR]

China's 'dual carbon' strategy is of strategic importance in leading global climate governance and promoting China's high-quality and green economic development. Cities are the centres of human activities. However, these are also places where high energy consumption and high carbon emissions are concentrated. This study selected data from 192 small and medium-sized cities in mainland China for the period 2000–2020. It classified these into seven categories using the K-means clustering method, calculated the decoupling status of different types of small and medium-sized cities based on the Tapio decoupling index, and analysed the relationship between carbon emissions and economic growth in various types of small and medium-sized cities based on the EKC curve theory. Finally, the backpropagation neural network (BPNN) model was used to classify and predict the time required to achieve carbon peaking for seven types of small and medium-sized cities. The following conclusions were drawn: (1) The development status of small and medium-sized cities shows a 'geese echelon pattern'. New dynamic small and medium-sized urban areas may reach peak carbon emissions first, while resource-based small and medium-sized urban areas may be the last to do so. (2) The decoupling of carbon emissions from industrial development in small and medium-sized cities has not been completed. This manifests mainly through the states of declining decoupling, absolute decoupling, and strong negative decoupling. (3) A significant inverted U-shaped relationship exists between economic development and carbon emissions in these cities. (4) These cities may have the disadvantage of premature or excessive de-industrialisation. Additionally, these generally have an inefficient and expansionary economic development status. Therefore, this study contended for a more robust integration of a radical political economy and ecological economics to support the development of such cities in China. The paper proposes differentiated development paths and fundamental recommendations for small and medium-sized cities in the implementation of the 'double carbon' strategy. The intention is to provide theoretical guidance and implications for promoting green and low-carbon development in urban and rural areas and for the early achievement of the strategy in China. [ABSTRACT FROM AUTHOR]

How to achieve the "double carbon" goal in energy systems has been the concern of governments. Integrated energy system (IES) is affected by climate change during his operation, in order to study the impact of climate change on IES and achieve the "double carbon" goal in energy systems, this paper proposes an integrated machine learning(IML) to forecast the long-term load, then investigates IES costs and carbon emissions in relation to climate, followed by the establishment of carbon peak energy system(CPES) and carbon neutral energy system(CNES), finally the honey badger algorithm is used to optimize the configuration of CPES and CNES. The results show that: IML can accurately make load forecasts. Under climate change, changes in load reduce the cost and carbon emissions of IES, and changes in equipment efficiency increase the cost and carbon emissions of IES. When both are considered, the cost and carbon emissions of IES increase by 1.18% and 0.92% per decade respectively. The costs of CPES and CNES increase by 0.93% and 1% respectively for every 10 years earlier than the year of achievement. To meet China's "double carbon" goal, CPES and CNES need to increase their costs by 1.97% and 2% respectively. • Integrated machine learning proposed for long-term load forecasting. • Studied the impact of system equipment efficiency and load with climate change. • Proposing carbon peaking energy system to achieve the carbon peak goal. • Proposed carbon-neutral energy system to achieve carbon neutrality goal. • The increased costs of achieving carbon peak and carbon neutral were studied. [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

To support high-quality carbon peaking, a scenario analysis of carbon peaking pathways according to an industrial chain perspective considering both material and part production needs exploring. As a critical segment in the aluminum industrial chain, aluminum casting industry was focused on in this paper. A bottom-up scenario analysis approach was proposed for the carbon emission simulation of China's aluminum casting industry, including: (1) a carbon emission assessment model based on material flow analysis and life-cycle assessment, (2) key influence factor selection and variation scenario prediction, (3) static and dynamic scenario analysis, and (4) driving factor decomposition analysis. Key findings are as follows: (1) China's aluminum casting industry accounted for about 1.1% of the national carbon emissions in 2020; (2) the production stage accounted for around 15% of the total carbon emissions of China's aluminum casting industry in 2020; (3) carbon peaking probabilities before 2030 for the static and dynamic scenario analysis are around 60% and 35%, respectively; and (4) the carbon peaking result differences in whether considering the production stage are about 20% for the static simulation and average 40% for the dynamic simulation. These findings effectively support the formulation of a high-quality carbon peaking pathway. • A bottom-up approach of scenario analysis was proposed for carbon peaking pathways. • Carbon emission assessment model was built with corresponding inventory data. • Static and dynamic scenario analyses of carbon emissions were compared. • Decomposition and sensitivity analysis of driving factors were conducted. • Production stage accounted for around 15% of the total carbon emissions. [ABSTRACT FROM AUTHOR]

Energy finance is an important market instrument for carbon peaking, which may influence the energy vulnerability. This paper explores the impact of two energy finance policies combined with renewable energy subsidy (RES) on energy vulnerability under different carbon peaking scenarios. To this end, the total fossil energy and renewable energy consumption under different carbon peaking scenarios are calculated using a computable general equilibrium (CGE) model. Then, the sector-level energy use rights are allocated through the game equity fixed cost allocation model which considers equity, game and efficiency principles. Combining the consumption thresholds of renewable energy, the impacts of energy finance policies on energy vulnerability under different carbon peaking scenarios are investigated using an energy finance CGE model. The main results are as follows. (1) In the long run, the earlier the peaking time, the lower the energy vulnerability. (2) Under each peaking scenario, no energy finance policy implemented can minimize energy vulnerability before carbon peaking. After the peaking year, the implementation of energy finance policies can reduce energy vulnerability more significantly. (3) Among the three types of policy coupling , energy finance policies combined with RES can minimize national energy vulnerability and buffer the impact of energy finance policies on GDP. [Display omitted] • The earlier the peaking time, the lower the energy vulnerability composite index. • Energy finance helps reduce more energy vulnerability under earlier carbon peaking. • Base scenario would minimize energy vulnerability before carbon peaking. • RES embedded in energy finance can minimize national energy vulnerability. • Combining RES with energy finance buffers the negative impact on GDP. [ABSTRACT FROM AUTHOR]

In order to deal with the increasing environmental challenges, the high carbon-emitting transport system keeps transitioning to achieve carbon peaking and carbon neutrality goals. This paper addresses the calculation of carbon emission estimation and identifying carbon reduction driving factors for the hinterland-based container intermodal network. Firstly, an analysis of the carbon emissions composition of the hinterland-based container intermodal networks is conducted. Furthermore, a carbon emission estimation model for the hinterland-based container intermodal network is developed. Subsequently, taking the Shanghai port and the Yangtze River Delta (YRD) hinterland as a case, we estimate the carbon emission of the hinterland-based container intermodal network under the Well-to-Wheel (WTW) and the Tank-to-Wheel (TTW) modes in the past ten years. Finally, the critical driving factors are identified by sensitivity analysis, and the variation of carbon emissions under different scenarios with carbon reduction policies are analyzed. The results indicate that the transportation structure and the fuel choices affect network emissions reductions significantly. In the current policy-based scenario, the WTW mode achieves the carbon peaking between 2030 and 2035, while the TTW mode peaks earlier. [ABSTRACT FROM AUTHOR]