Achieving carbon neutrality in China before 2060 requires a radical energy transition. To identify the possible transition pathways of China’s energy system, this study presents a scenario-based assessment using the Low Emissions Analysis Platform (LEAP) model. China could peak the carbon dioxide (CO₂) emissions before 2030 with current policies, while carbon neutrality entails a reduction of 7.8 Gt CO₂ in emissions in 2060 and requires an energy system overhaul. The assessment of the relationship between the energy transition and energy return on investment (EROI) reveals that energy transition may decrease the EROI, which would trigger increased energy investment, energy demand, and emissions. Uncertainty analysis further shows that the slow renewable energy integration policies and carbon capture and storage (CCS) penetration pace could hinder the emission mitigation, and the possible fossil fuel shortage calls for a much rapid proliferation of wind and solar power. Results suggest a continuation of the current preferential policies for renewables and further research and development on deployment of CCS. The results also indicate the need for backup capacities to enhance the energy security during the transition.

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads, how to maintain the stable, reliable, and efficient operation of the power system has become a challenging issue requiring investigation. One of the feasible solutions is deploying the energy storage system (ESS) to integrate with the energy system to stabilize it. However, considering the costs and the input/output characteristics of ESS, both the initial configuration process and the actual operation process require efficient management. This study presents a comprehensive review of managing ESS from the perspectives of planning, operation, and business model. First of all, in terms of planning and configuration, it is investigated from capacity planning, location planning, as well as capacity and location combined planning. This process is generally the first step in deploying ESS. Then, it explores operation management of ESS from the perspectives of state assessment and operation optimization. The so-called state assessment refers to the assessment of three aspects: The state of charge (SOC), the state of health (SOH), and the remaining useful life (RUL). The operation optimization includes ESS operation strategy optimization and joint operation optimization. Finally, it discusses the business models of ESS. Traditional business models involve ancillary services and load transfer, while emerging business models include electric vehicle (EV) as energy storage and shared energy storage.

The diffusion of new energy vehicles (NEVs), such as battery electric vehicles (BEVs) and fuel cell vehicles (FCVs), is critical to the transportation sector’s deep decarbonization. The cost of energy chains is an important factor in the diffusion of NEVs. Although researchers have addressed the technological learning effect of NEVs and the life cycle emissions associated with the diffusion of NEVs, little work has been conducted to analyze the life cycle costs of different energy chains associated with different NEVs in consideration of technological learning potential. Thus, relevant information on investment remains insufficient to promote the deployment of NEVs. This study proposes a systematic framework that includes various (competing or coordinated) energy chains of NEVs formed with different technologies of power generation and transmission, hydrogen production and transportation, power-to-liquid fuel, and fuel transportation. The levelized costs of three typical carbon-neutral energy chains are investigated using the life cycle cost model and considering the technological learning effect. Results show that the current well-to-pump levelized costs of the energy chains in China for BEVs, FCVs, and internal combustion engine vehicles (ICEVs) are approximately 3.60, 4.31, and 2.21 yuan/GJ, respectively, and the well-to-wheel levelized costs are 4.50, 6.15, and 7.51 yuan/GJ, respectively. These costs primarily include raw material costs, and they vary greatly for BEVs and FCVs from resource and consumer costs. In consideration of the technological learning effect, the energy chains’ well-to-wheel levelized costs are expected to decrease by 24.82% for BEVs, 27.12% for FCVs, and 19.25% for ICEVs by 2060. This work also summarizes policy recommendations on developing energy chains to promote the diffusion of NEVs in China.

China is breaking through the petrodollar system, establishing RMB-dominating crude oil futures market. The country is achieving a milestone in its transition to energy finance market internationalization. This study explores the price leadership of China’s crude oil futures and identifies its price co-movement to uncover whether it truly shakes up the global oil spots market. First, we find that for oil spots under different gravities, China’s oil futures is only a net price information receiver from light-, medium-, and heavy-gravity oil spots, but it has a relatively stronger price co-movement with these three spots. Second, for oil spots under different sulfur contents, China’s oil futures still has weak price leadership in sweet, neutral, and sour oil spots, but it has strong co-movement with them. Third, for oil spots under different geographical origins, China’s oil futures shows price leadership in East Asian and Australian oil spots at the medium- and long-run time scales and strong price co-movement with East Asian, Middle Eastern, Latin American and Australian oil spots. China’s oil futures may not have good price leadership in global spots market, but it features favorable price co-movement.

The term environmental innovation system refers to an innovation network composed of enterprises, universities, and research institutions involved in the development and diffusion of environmental technology, with the participation of a government. An environmental innovation system not only exerts important impact on the achievement of carbon neutrality but also affects social and economic activities. Investigations on environmental innovation system performance constantly assume a single-stage independent system while ignoring its internal structure. However, such systems are composed of environmental innovation research and development (R&D) and environmental innovation conversion subsystems. A two-stage data envelopment analysis (DEA) model is developed in this study to analyze the efficiency of Chinese regional environmental innovation system by opening the “black box” and considering shared resources. Empirical results indicated that China presents high overall environmental innovation efficiency although some regions need to improve. Regions with low efficiencies in both environmental innovation R&D (EIR) and environmental innovation conversion (EIC) subsystems should expand their investment in and strengthen the management of environmental innovation resources. Regions with low EIR efficiency should improve the absorption and transformation of environmental innovation achievements. Regions with low EIC efficiency should increase investment in the commercialization of environmental innovation achievements and encourage green economy industries, such as new energy, art, tourism, and environmental protection.

The rebound effect refers to the phenomenon that individuals tend to consume more energy in the face of energy efficiency improvement, which reduces the expected energy-saving effect. Previous empirical studies on the rebound effect of regions and sectors do not provide microscopic evidence. To fill this gap, we use China’s firm-level data to estimate the rebound effect in China’s manufacturing subsectors, providing a detailed picture of China’s rebound effect across different sectors and different regions in 2001–2008. Results show that a partial rebound effect robustly appears in all industries, and the disparity between sectors is quite broad, ranging from 43.2% to 96.8%. As for the dynamic rebound effect of subsectors, most subsectors present an upward trend, whereas few subsectors show a clear downward trend. As a whole, the declined trend of the rebound effect is driven by the descent of minority sectors with high energy consumption and high energy-saving potential. In addition, we find that the disparity of the rebound effect across sectors is more significant than that across regions.

This study combines multi-regional input–output (MRIO) model with linear programming (LP) model to explore economic structure adjustment strategies for the reduction of carbon dioxide (CO₂) emissions. A particular feature of this study is the identification of the optimal regulation sequence of final products in various regions to reduce CO₂ emissions with the minimum loss in gross domestic product (GDP). By using China’s MRIO tables 2017 with 28 regions and 42 economic sectors, results show that reduction in final demand leads to simultaneous reductions in GDP and CO₂ emissions. Nevertheless, certain demand side regulation strategy can be adopted to lower CO₂ emissions at the smallest loss of economic growth. Several key final products, such as metallurgy, nonmetal, metal, and chemical products, should first be regulated to reduce CO₂ emissions at the minimum loss in GDP. Most of these key products concentrate in the coastal developed regions in China. The proposed MRIO–LP model considers the inter-relationship among various sectors and regions, and can aid policy makers in designing effective policy for industrial structure adjustment at the regional level to achieve the national environmental and economic targets.

This study extends the ambit of the debate on electricity transition by specifically identifying possible policy entry points through which transformative and enduring changes can be made in the electricity and socio–economic systems to facilitate the transition process. Guided by the “essence” of the multi-level perspective — a prominent framework for the study of energy transition, four such entry points have been identified: 1) destabilising the dominant, fossil fuel-based electricity regime to create room for renewable technologies to break through; 2) reconfiguring the electricity regime, which encompasses technology, short-term operational practices and long-term planning processes, to improve flexibility for accommodating large outputs from variable renewable sources whilst maintaining supply security; 3) addressing the impact of coal power phase-out on coal mining regions in terms of economic development and jobs; and 4) facilitating a shift in transition governance towards a learning-based, reflexive process. Specific areas for policy interventions within each of these entry points have also been discussed in the paper.

Promoting the growth of the lithium battery sector has been a critical aspect of China’s energy policy in terms of achieving carbon neutrality. However, despite significant support on research and development (R&D) investments that have resulted in increasing size, the sector seems to be falling behind in technological areas. To guide future policies and understand proper ways of promoting R&D efficiency, we looked into the lithium battery industry of China. Specifically, data envelopment analysis (DEA) was used as the primary approach based on evidence from 22 listed lithium battery enterprises. The performance of the five leading players was compared with that of the industry as a whole. Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019. However, during this period, a significant increase in R&D expenditure was witnessed. This finding was supported, as the results showed that the average technical efficiency of the 22 enterprises was 0.442, whereas the average pure technical efficiency was at 0.503, thus suggesting that they were suffering from decreasing returns to scale (DRS). In contrast, the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry’s average. Moreover, they were experiencing increasing scale efficiency (IRS). We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale; rather, it should also encompass assisting businesses in developing efficient managerial processes for R&D.

Electricity consumption is one of the major contributors to greenhouse gas emissions. In this study, we build a power consumption carbon emission measurement model based on the operating margin factor. We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects (i.e., emission intensity, power generation structure, consumption electricity intensity, economic scale, population structure, and population scale) and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China’s economic development and population changes. Moreover, we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies. The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.