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Carbon neutrality is an important policy in the current global response to climate change and has been widely recognized by various industries. In the process of promoting carbon neutrality, new energy plays a pivotal role. In this study, the definition and connotation of new energy and its role and specific operation in the energy transition of carbon neutrality are firstly explained. Promoting new energy development requires significant green and low-carbon investments. Taking China as an example, this paper analyzes the opportunities brought by the carbon neutral process to the field of green finance and analyzes the main features and development trends of green finance in China at present. Then this paper proposes policy recommendations to strengthen the development of green finance in China in terms of improving the green financial policy system, enhancing the supply capacity of green financial services, and optimizing the supporting environment for green financial development. Finally, this paper analyzes the measures and experiences of the United States in promoting low-carbon development and proposes countermeasures for China's low-carbon development on the basis of the five major relationships that need attention in China's carbon-neutral process. That is, strengthen the top-level design and improve the regulatory policy system; optimize the energy structure and increase the proportion of clean energy; optimize the industrial structure and reduce energy consumption in key industries; build a complete low-carbon technology system and promote low-carbon technology research and development and demonstration applications, and encourage local conditions to explore low-carbon development paths. The development of green finance can contribute to the advancement of new energy technologies, thus contributing to the achievement of carbon neutrality goals. [ABSTRACT FROM AUTHOR]

This paper proposes an extended Solow decomposition framework with a finite mixture model, which is incorporated to account for heterogeneous regimes. The model decomposes green economy into two different components, namely, green total factor productivity and factor endowment. Then, we reestimate the growth sources of green economy and especially the importance of energy consumption in China's provincial industries over 2000-2015. The empirical results suggest that there are three green growth regimes across Chinese provinces for overall economy and different industries. Specifically, some provinces switch regime over time, while the others maintain the same regime. Furthermore, when controlling for regime heterogeneity, the contribution of factor endowment decreases and that of green total factor productivity increases. With respect to energy consumption, traditional methods overestimate its importance in the primary and tertiary industries and underestimate that in the secondary industry. In addition, the reliance of China's overall economy and the secondary and tertiary industries on energy consumption decreases as economy develops. [ABSTRACT FROM AUTHOR]

This paper examines the CO2 emissions from energy consumption in China's transport sector, conducting an empirical investigation into the spatial distribution characteristics and influencing factors of transport CO2 emissions. This study, which is based on province-level panel data covering the 30 provincial regions during the period 2001-2016, used the methods of exploratory spatial data analysis (ESDA) and the extended STIRPAT model (examined by the method of system-generalized method of moments (Sys-GMM) regression). The results indicated that the amount of CO2 emissions in China's transport sector has increased steadily during the observation period, but there was a noticeable disparity across the provinces and regions. From the perspective of spatial dimension, the spatial agglomeration characteristics of provincial transport CO2 emissions tended to be strengthened, and the pattern evolutions of spatial distribution presented a path-dependence effect to some extent. The scale of population was found to be the most important influencing factor of transport CO2 emissions, and followed by the per-capita GDP. Further, the improvement of energy efficiency was the key factor to controlling transport CO2 emissions. Compared to freight transportation, passenger transportation was more important in transport CO2 emissions reduction due to its lower efficiency of energy utilization and rapid growth. Meanwhile, electrification played an important inhibitory effect on transport CO2 emissions because of its high fuel efficiency and less pollution. Importantly, we could not support the existence of the environmental Kuznets curve (EKC) hypothesis in China's transport sector during the observation period, which describes the relationship between the environmental pressures and economic development. These findings contain some meaningful implications for policy makers: confirm the priority transport CO2 emissions reduction areas, improve transport energy efficiency, strengthen passenger transportation decarburization policy, and highlight the model shift of fuel consumption. [ABSTRACT FROM AUTHOR]

As part of the country's efforts to achieve green development, China implemented a mandatory energy intensity reduction target in its 11th "Five-Year Plan (FYP)" in 2006, and then began to roll out a series of relevant measures. However, existing studies have paid little attention to the actual effects of China's energy intensity constraint policy (EICP). In this paper, using panel data from China's 36 industrial sub-sectors covering the years from 2001 to 2014, we adopt the difference-in-differences (DID) method to investigate for the first time the EICP's (marginal) effect on total factor energy efficiency growth (TFEEG). We also estimate the superposition effect caused by the introduction of a carbon intensity constraint policy (CICP) on TFEEG, through the difference-in-difference-in-differences (DDD) strategy. Finally, using counterfactual, re-grouping and quasi-DID analyses, we conduct a series of robustness tests of the empirical results. The results show that the TFEEG in China's industrial sector experienced an overall declining trend between 2001 and 2014. The implementation of the EICP has had a significantly negative effect on the improvement of the TFEEG of sub-sectors with higher levels of energy intensity. After the implementation of the EICP, the TFEEG rate of these sub-sectors declined by 4.31%, compared to the rate of the other sub-sectors. The results of a series of robustness tests indicate that such a negative effect is credible. The marginal effect in the first two years after the implementation of the EICP was significantly negative, while the superposition effect of the introduction of a CICP on industrial TFEEG remained negative. Thus, the Chinese government should reinforce the implementation of energy-saving policies by introducing additional market-oriented auxiliary policies to propel the green development transformation of China's industrial sector. [ABSTRACT FROM AUTHOR]

With the development of economy, the impression of energy and environment is increasing, and improving energy efficiency plays a vital role in the sustainable economic development in China. An application of data envelopment analysis (DEA) and Malmquist indices has been used in this paper to investigate energy utilization efficiency of 30 provinces, autonomous regions, and municipalities (apart from Hong Kong, Macau, Taiwan, and Tibet) in China. The results show that industries in the eastern area have the best average energy efficiency for the period 2006-2009, followed by the central area. The industrial energy overall technical efficiency, industrial energy pure technical efficiency, industrial energy scale efficiency, etc. in 30 different regions of China are examined. By the comparative analysis of energy efficiency, the results show that the energy efficiency of Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong, and Hainan provinces is effective, and the lowest six regions of energy efficiency are Ningxia, Inner Mongolia, Shaanxi, Liaoning, Chongqing, and Jilin, whose energy efficiency is in the level of 0.5 or so. The results mean that the eastern regions are better than the central and western regions according to energy efficiency. [ABSTRACT FROM AUTHOR]

The trade-off between economic growth and ecological improvement has always become an important and difficult issue for many countries, especially for developing countries. Due to a long-term extensive economic growth pattern, the regional resource allocation deviates from the optimal, especially the existence of energy misallocation, which hinders the maximization of economic output. Therefore, considering the characteristics and heterogeneity of resource endowments in different regions and increasing renewable energy consumption, that is, promoting energy transition, is it capable of sustainable development under China's actual conditions? The exploration of the issue is a core step in the research of the impact of renewable energy on industrial green transformation. Based on the panel data of 30 regions in China from 2009 to 2016, this paper constructs a threshold model from the perspective of regional energy misallocation and empirically tests the nonlinear mechanism of renewable energy consumption to promote industrial green transformation. The results show that China's energy allocation efficiency is low, there is a certain misallocation phenomenon, and the improvement effect in recent years is not satisfactory. Further, the relationship between renewable energy consumption and industrial green transformation is not a simple linear relationship, but a double threshold effect due to regional energy misallocation. In areas with severe energy misallocation, renewable energy consumption does not have a significant boost to industrial green transformation. Finally, this paper proposes the policy enlightenment of promoting industrial green transformation from the aspects of performance evaluation, market reform, and factor flow. [ABSTRACT FROM AUTHOR]

PM 2.5 emissions has become a serious issue in China with enormous regional variation in economic development, resource endowment, and consumption patterns, which leads to great variations in regional PM 2.5 emissions and embodied PM 2.5 transfer among provinces. The spatial and temporal evolution pattern of PM 2.5 and its influencing factors are important issues in the study of air pollution emissions and control, which could help policy makers allocate emission responsibilities and reduction targets. Based on the perspective of final consumption, this paper uses a multiregional input-output model to estimate PM 2.5 emissions in 30 provinces in different years in China, applies a geographically weighted regression model to analyze different driving factors, and then analyzes the spatial heterogeneity of consumption-based PM 2.5 emissions. The results show that consumption-based PM 2.5 presents a significant reduction trend, although the separation of production and consumption areas of PM 2.5 emission is further intensified. The provinces with rich energy but relatively backward economies and manufacturing industries correspond to the primary net inflow of PM 2.5 , while the provinces with strong consumption ability and the most developed economies and industries present a net outflow of PM 2.5. Moreover, the slowdown of consumption growth and the improvement of consumption quality have a positive impact on PM 2.5 emission reductions. The optimization of industrial structure has a more significant impact on pollution control and haze reduction in the western region, while the improvement of energy utilization efficiency and rational use of construction land are more conducive to the reduction of consumption-based PM 2.5 emissions in the eastern region. From the perspective of temporal and spatial changes, significant spatial differences occur between regions. Energy consumption, environmental protection investment and construction land show the trend of " high value area diffusion and low value area reduction". • Explores the spatial distribution characteristics of PM 2.5 emissions in China from the perspective of consumption. • Consider and analyze the inter-annual variation of pollutant emission transfer. • Clarifies the spatial heterogeneity characteristics of influencing factors. • Emphasizes the importance of further optimizing influencing factors to reduce haze from the consumption end. [ABSTRACT FROM AUTHOR]

As China takes great efforts to cap its total energy consumption, it is important to understand the future energy use in all sectors. This paper aims to present a long-term prediction of energy use in China's construction and building sectors (CBS) up to the year 2100. A STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is used to establish the relationship between six socioeconomic and technological factors and China's CBS energy consumption. Based on the statistical data from 2000 to 2016, ridge regression is applied to derive the coefficients of the STIRPAT model to counter the impact of multicollinearity on regression results. The projections are performed for three scenarios: a benchmark scenario, an intensive scenario, and an extensive scenario. The results show that for all three scenarios, the overall trend of China's CBS energy consumption is to continuously increase from the present, reach a peak in the range between 1155 and 1243 million tons of standard coal equivalent (Mtce) in 2050, and then decrease to 942–1116 Mtce in 2100. The above projection and the associated STIRPAT model are valuable for developing policies on construction and buildings to control the total energy use in China. • Use a STIRPAT model to predict China's energy use in construction and buildings. • Three scenarios and six socioeconomic and technological factors are considered. • The projected energy will increase until 2050 and then decrease gradually. • A peak energy use between 1155 and 1243 Mtce is projected to occur in 2050. [ABSTRACT FROM AUTHOR]

This paper conducts panel analysis to evaluate the effects of a structural economic shift from the industrial to the tertiary sector, a reduction in industrial overcapacity, and improvements in energy efficiency on energy consumption using data for 30 Chinese provinces from 1995 to 2015. We find that, at the national level, the structural shift to the tertiary sector, the reduction in cement and steel production, and the increase in energy efficiency in the industrial sector all have statistically significantly negative effects. We also divide the sample into three geographic and economic regions to evaluate regional differences. We find that the gross domestic product (GDP) share of the tertiary sector shows its greatest impact on reducing energy consumption in the eastern region, a decline in heavy industry production would reduce energy demand more in the central region, and improvement in industrial electricity efficiency would also help reduce energy consumption the most in eastern China. We also forecast energy consumption in China will reach 4.8–4.9 billion tonnes of coal equivalent (tce) in 2020 and further grow to 5.0–5.4 billion tce in 2030. [ABSTRACT FROM AUTHOR]