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The carbon neutrality agenda requires that carbon lock-in enters an endgame. A well-developed energy system, which is secure, equitable, and sustainable is paramount to getting rid of carbon lock-in and realizing decarbonization. We carry out systematic research to empirically study the possible carbon lock-in reduction effect of energy trilemma based on the instrumental variable-generalized method of moment (IV-GMM) model. We also detect heterogeneous effects in terms of geographical locations and endowments of capital, as well as the impact mechanisms. Moreover, this paper highlights the mediating and threshold role of environmental regulation. We thus get the following conclusions. (1) The paper's primary findings underscore that the continuous increase of energy trilemma exacerbates carbon lock-in and hampers the process of decarbonization; (2) Energy trilemma has a higher stimulating effect on carbon lock-in in provinces that lie in the central region and that have a comparatively low level of physical, social, and human capital; (3) Energy trilemma can also indirectly aggravate carbon lock-in by inhibiting technology innovation and exacerbating carbon emissions intensity; (4) The interaction of environmental regulation and energy trilemma can alleviate the energy trilemma - carbon lock-in nexus, and environmental regulation also shows a significant threshold effect. • We investigate the impact of the energy trilemma on carbon lock-in. • Energy trilemma eradication is an effective way to realize the carbon lock-in endgame. • The nexus between energy trilemma and carbon lock-in is more prominent in provinces with high capital endowments. • Environmental regulation shows significant mediating and threshold effects. • The exacerbate carbon lock-in effect of energy trilemma depends on technology and intensity effect. [ABSTRACT FROM AUTHOR]

The carbon market has been playing an increasingly important role in mitigating carbon emissions. However, with the tightening of the carbon allowance, where the carbon market will evolve is still unclear. Abstracting three agents—Government, Carbon-supplier('CS'), Carbon-buyer('CB'), this paper intends to investigate the operation mechanism of carbon market from the perspective of the stakeholders. Based on the simulation analysis, it is suggested that: 1) the carbon market in China may become a major "seller's market" when carbon quota experienced dramatic reduction; 2) when the carbon price is constant, CS with excessive carbon productivity will crowd out and exploit CB in the carbon market competition; 3) strict administrative penalties and information disclosure would help promote proactive participation of CS and CB in the carbon market; and 4) the interaction between CS and CB in different strategic scenarios is influenced by the carbon price and the coefficient of demand variation for carbon quota. In contrast with previous studies, this paper has uncovered a dynamic strategic evolution mechanisms of stakeholders in the carbon market at the micro-level, contributing to carbon market governance and providing stakeholders with tailor-made climate policy tools and strategic choices. • The carbon market in China may evolve into a "seller's market" with a scarcer allowance. • We first identify the phenomenon of carbon exploitation in the carbon market. • Improving the carbon price under buyer's market would facilitate positive participation of three agents. • Carbon accounting, carbon disclosure, and strict administrative penalty help regulate non-compliance actions by enterprises. • The equilibrium of agents' strategy are influenced by dual aspects—parameters and other stakeholders' choices. [ABSTRACT FROM AUTHOR]

Transferring energy-intensive industries to regions with abundant clean energy is crucial for achieving low-carbon transformation of energy-intensive sectors during carbon neutrality. By manually collecting the project data from energy-intensive enterprises in China, we construct a three-dimensional panel data with 1.86 million observations to empirically examines the impact of the carbon intensity constraint policy (CICP) on the transfer of high energy-intensive projects (HEP). Our findings indicate that the CICP accelerates the transfer of HEP. The transfer direction shows that the HEP transferred from the eastern region to the western region have increased, but the HEP transferred from the western region to the eastern region have not decreased. To explain the above phenomena, we further discuss the underlying mechanism from the perspective of energy structure and industrial agglomeration. From the standpoint of transfer-out regions, the higher the proportion of thermal power in the energy consumption structure, the more HEP will be transferred out. From the standpoint of transfer-in regions, the industrial agglomeration will attract the transfer of the HEP. However, we do not find evidence that wind power and photovoltaics can attract the HEP. This paper provides project-level empirical evidence for promoting the low-carbon transformation of energy-intensive industries. • The carbon intensity constraint accelerates the transfer of energy-intensive projects. • The dynamic effect shows a fluctuating and decreasing trend. • The transfer direction is the net transfer from the east to the west. • The more thermal power, the more energy-intensive projects will be transferred out. • We don't find the attraction of the clean energy for the energy-intensive projects. [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]

COVID-19 brings new challenges to addressing climate change and achieving the goal of environmental sustainability. This paper examines how the COVID-19 pandemic affects transmission of returns spillovers among green finance, carbon market and renewable & non-renewable energy in China. We evaluate the dynamic and directional risk connectedness across time and at various frequency bands using Diebold and Yilmaz and Baruník and Křehlík approaches. First, the total spillover effects increase significantly when the COVID-19 outbreak. Second, the carbon price fluctuations are mainly influenced by non-renewable energy. Third, the spillover effect is mainly acted in the short term, meaning that it is strongly influenced by investor sentiment in the short term. Fourth, non-renewable energy and carbon markets are the main net recipients and can be used as hedge assets. Finally, the total spillover is time-varying that is affected by the unexpected crisis. The findings will help policymakers and environmental investors to develop policy measures and portfolios that are appropriate for different frequency domains. • Time-frequency connectedness among green finance, carbon and energy is tested. • Spillovers increase at the onset of the COVID-19 and mainly acted in the short-term. • The carbon price fluctuations are mainly influenced by non-renewable energy. • Non-renewable energy and carbon markets are the major net recipients. • The total spillover is time-varying affected by the unexpected crisis. [ABSTRACT FROM AUTHOR]

With rapid economic development and expansion of built environment since the year 2000, urbanization has led to great challenge of reduction targets in energy intensity and carbon dioxide emissions. This paper aims to explore the disaggregate driving force of different sectors and investigate the potential of carbon dioxide reduction of them in the context of rapid urbanization. By using the panel datasets at a provincial level in a LMDI (Logarithmic Mean Divisia Index) model, we investigated the effects of various energy-related and socioeconomic factors on regional carbon dioxide emissions in 30 China's provinces from 2000 to 2013. The results show that the factors of economic development are the largest drivers of regional carbon dioxide emissions, compared with the factors of structural change, energy intensity, and social transition. Meanwhile, evidence is found that the urbanization process has contributed to the regional CO 2 emissions in China's provinces through a variety of approaches, including changing the energy use characteristics of urban households, transportation, and business sectors. Finally, we found that the changing trend of these influence factors' effect on regional CO 2 emissions varied across different provinces, which means there are significant regional heterogeneity in the influence mechanisms of urbanization process on regional CO 2 emissions. [ABSTRACT FROM AUTHOR]

This paper studies static and dynamic connectedness among carbon, traditional (oil and coal)/new energy and material (iron, aluminum, cement, and plastic) markets based on the Diebold Yilmaz (2012) method and the Barunik and Krehlik (2018) method. First, total connectedness is larger in the short term and enhanced after the COVID-19 outbreak. Second, material markets exhibit higher explanatory power; plastic prices, for example, have played a leading role during the pandemic crisis according to the networks. Third, the spillover of China's carbon markets post-outbreak is about twice as large as pre-outbreak, showing positive net connectedness in the medium and long term. Carbon markets' spillover is relatively small and mainly contributes to new energy. Finally, time-varying analysis demonstrate that the positive and negative values of time-varying connectedness peak due to economic shocks or global emergencies. High connectedness with longer duration may be caused by a sudden emergency rather than anticipated events. These findings can offer significant benefits to policymakers, high-carbon businesses and investors in crafting appropriate strategies with heterogeneous frequency horizons. • Spillovers among carbon, traditional/new energy and material markets are examined before and during the COVID-19. • Spillover effects of materials are strongest. • China's carbon market's spillover is small, and carbon prices have positive net connectedness in the medium and long term. • New energy has become the main net transmitter during the COVID-19 pandemic. • Spillovers with longer duration are brought by unexpected crisis rather than anticipated events. [ABSTRACT FROM AUTHOR]

China's carbon emissions have been ranking first in the world. This study filled in the gaps in research, decomposed carbon intensity from the perspective of time, space and industry. A decoupling effort model based on factor decomposition models was constructed to analyze the driving factors of carbon emissions and economic decoupling, which builded a foundation for achieving sustainable economic development. Using the Logarithmic Mean Divisia Index method (LMDI), the paper measured the carbon emission intensity of 29 provinces and cities in China from 1998 to 2019, and decomposed the decoupling effect between GDP and carbon emission on the basis of factor decomposition by tapio. The results showed that: (1) Carbon intensity declined first, then rise lightly, and finally declined steadily. For the primary industry and the tertiary industry, the carbon intensity declined steadily, while the carbon intensity increased accordingly to the overall carbon intensity. In terms of spatial evolution, the regional differences between different provinces decreased correspondingly. (2) The cumulative contribution rates of these three effects, i.e., technological progress, industrial structure and regional scale were 106.3299%, −15.1486% and 8.8188%, respectively. There were obvious differences of these cumulative contribution rates of carbon intensity among different provinces. (3) From the perspective of industrial, technological progress effect is the largest contribution for carbon intensity in the secondary industry. The Industrial structure effect mainly affects the primary and tertiary industries; and no significant difference in regional scale effect. (4) The decoupling effect gradually improved, and technological progress has played an absolute leading role in promoting the decoupling effect. Based on the research results, the key policy recommendation are put forward as follows: (1) Further improve the technological level and support clean technology enterprises. (2) Promote industrial upgrading in backward industrial provinces (3) Promote regional assistance and the introduction of high-quality foreign investment. • A decoupling effort model built based on the Logarithmic Mean Divisia Index model. • Carbon intensity declined first, then rise lightly, and finally declined steadily. • Technological progress effect dominates the carbon intensity and decoupling effect. • Industrial structure effect mainly affects the primary and tertiary industries. [ABSTRACT FROM AUTHOR]

Abstract: China is a large developing country with high carbon intensity. It is likely that China will have to face the challenge of CBTA (carbon motivated border tax adjustments) in future. Meanwhile, CBTA would create price gap between CBTA users and target countries, and change the competitiveness of different producers. Under such circumstances, this paper seeks to contribute to the debate on CBTA by focusing on the potential impacts of CBTA on different regions of China from a perspective of competitiveness. We adopt two geographical divides in China (eastern–central–western zones, high-trade-openness to low-trade-openness regions), and simulate the potential impacts across regions through a general equilibrium model. Our simulation results show that CBTA would affect competitiveness of different producers, their comparative advantages, relative trade shares, outputs and emissions. CBTA would induce structure change of the economy, and result in a shift of industrial output toward non-industrial output. There are significant differences in the effects of CBTA among different regions in China. CBTA would result in a relocation of outputs across regions in China. Therefore, CBTA would also have impact on China’s regional development policy. [Copyright &y& Elsevier]

Energy and carbon emission performance evaluation is a focus topic in the field of environmental protection and energy utilization. However, the current studies are not high in identifying economic output inefficiency and neglect to control target energy carbon emission intensity (ECI), which may cause evaluation bias. Hence, this paper presents a constrained performance index measure (CPIM) model to solve the issues. The proposed model takes the performance index measure and can identify more numbers of positive economic output inefficiency compared to the directional distance function (DDF) model. Besides, the ECI constraint is presented and imposed into the proposed model to restrict the target ECI of decision making units. The numerical example from China's provincial dataset demonstrates and confirms the validity and merit of the proposed model through the measure and constraint comparison among different models. Then, it is found that most provinces exist certain economic output inefficiency and the center regions have a worst score of energy carbon emission performance index due to the rapid development of their secondary industry by using the proposed model. Several valuable modeling directions and questions related to performance index measure and energy pollution emission constraints are provided for future research. • We propose a constrained performance index measure (CPIM) model. • The CPIM model can identify more economic output inefficiency than the DDF model. • The CPIM model can restrict target energy carbon emission intensity of DMUs. • The numerical example demonstrates the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]