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Carbon neutrality is the only way for green low-carbon development. Technological progress is an important starting point for improving energy efficiency. Through scientific and technological innovation, improving energy efficiency is a practical way to achieve the goal of carbon neutralization. Due to the limitation of data and index selection, there is no qualitative analysis of carbon neutrality in this paper. Compared with previous studies, the paper has the following contributions: Firstly, the energy efficiency score of China's provincial level is measured with the DEA method and it is found that the average energy efficiency score of the eastern region is the highest, followed by the western region, and finally the central region. The results also show that the energy efficiency is not in accordance with the economic growth. The regions with higher energy efficiency scores are not always economically developed because of huge energy consumption supporting the economic development. Secondly, the Tobit method is used to study the impact of the reverse technology spillover effect of OFDI on China's energy efficiency. The results show that the spillover effect has a significant positive effect on the improvement of China's energy efficiency, and has a positive impact on the eastern and western regions, but has no significant impact on the western region. Finally, we put forward some policy recommendations to increase energy efficiency to approach carbon neutrality and thus promote green economic development. This paper enriches the content of energy economy, the research method is innovative, and the conclusion is more scientific. [ABSTRACT FROM AUTHOR]

Ports play an important role in transportation networks, but the ecological efficiency of many ports cannot reach an effective level. To improve the ecological efficiency of ports, we need to analyze the current ecological efficiency of ports and propose policy recommendations based on the results. In this paper, we first employed the super-efficiency SBM model to calculate the ecological efficiency of the four major ports in the Yangtze River Delta port group from 2011 to 2021. The results showed that the average comprehensive ecological efficiency of the four major ports was 0.647, which did not reach the effective level. The overall ecological efficiency curve was U-shaped. Then we used the Tobit model to analyze the key factors affecting the port's ecological efficiency. We found that the economic development of port cities could promote the improvement of port ecological efficiency. The application level of emerging technologies also had a positive impact, but the impact was weak. However, expanding the scale of the port would reduce its ecological efficiency. At last, we provided some policy recommendations. [ABSTRACT FROM AUTHOR]

Can China's carbon trading policy promote regional green total factor productivity? What are the specific influencing processes and mechanisms between the two? In this paper, we construct an indicator framework for China's green total factor productivity from the perspective of green development, which comprehensively considers various inputs and outputs. This paper uses a more scientific Super-EBM model considering the mixed distance to accurately measure the regional green total factor productivity in China. The static and dynamic effects and multiple mediating effects of carbon trading policy on green total factor productivity were studied by using DID model combined with stepwise regression method. The robustness of the results was tested by parallel trend test and placebo test. The mediating effects of total energy consumption, energy consumption structure, technological innovation and the moderating effects of financial development level and intellectual property protection degree are studied. The results show that carbon trading policy significantly improves China's regional green total factor productivity. The effect of carbon trading policy on green total factor productivity in pilot areas increased to decreased; Total energy consumption and energy consumption structure have a partial intermediary effect on the relationship between carbon emission trading policy and regional green total factor productivity in China, while technological innovation has a masking effect. Regional financial development level and intellectual property protection intensity play a moderating role in the relationship between carbon emission trading policy and regional total factor productivity. Based on the above conclusions, some suggestions are put forward for the improvement of China's carbon trading policy system. [ABSTRACT FROM AUTHOR]

In China, rapid development has lead to serious environmental pollution. Excessive carbon dioxide emissions have not only caused significant damages to the environment, but also harmed people's health. Subject to carbon peaking and carbon neutrality goals, cities need to conserve energy and reduce emissions while maintaining steady economic growth, an effective measure for which is to improve carbon efficiency. Carbon efficiency is affected by environmental regulation. As a kind of environmental regulation featuring public participation, environmental information disclosure has become an important measure affecting carbon efficiency. In this paper, we estimated carbon efficiency of Chinese cities with super-efficiency DEA model, analyzed the spatial and temporal heterogeneity of carbon efficiency. Unlike previous literature, we further investigated the impact of environmental information disclosure on carbon efficiency using difference-in-differences model, tested split samples by geographical location and city size to examine the impacts of environmental information disclosure on carbon efficiency in different regions and cities of different sizes, and validated that environmental information disclosure affects carbon efficiency through technological improvements and clean industry substitution. On this basis, we proposed policy measures to improve carbon efficiency, including transforming government functions, offering inter-regional carbon compensation, and establishing a diversified carbon emissions reduction system. [ABSTRACT FROM AUTHOR]

Under the Chinese government's carbon neutral target, digital transformation enabling low-carbon technology innovation has become an innovative starting point to promote the new development concept. We use data from a sample of 270 manufacturing companies in 30 provinces and cities in China, this paper constructs an analysis framework of low-carbon technology innovation in manufacturing driven by digital transformation with knowledge reconstruction as the intermediary variable and knowledge sharing as the regulating variable, and empirically explores the impact of digital transformation on lowcarbon technology innovation. It is found that the digital transformation of the manufacturing industry has a significant positive impact on low-carbon technology innovation, and is an important power source to promote low-carbon technology innovation. Knowledge reconstruction plays a part of intermediary role between digital transformation and low-carbon technology innovation, and plays a key role as a bridge. Knowledge sharing plays a positive role in digital transformation, knowledge reconstruction and low-carbon technology innovation. The research clarified the differences and suitability in the process of low-carbon technology innovation in the digitally enabled manufacturing industry and answered the question of how to effectively use knowledge management to realize digitalization and help the industrial low-carbon innovation and upgrading in the digital economy era, which has important reference significance for guiding the green and high-quality development of the industry. [ABSTRACT FROM AUTHOR]

To realize the great goal of attaining peak carbon in 2030 and carbon neutrality in 2060, agricultural carbon mitigation must be an essential activity in China. Policy-oriented agricultural insurance is accepted as an effective income guarantee and risk transfer tool, which can not only disperse the risk of agricultural operation but also guide green agricultural production. In this study, based on panel data from 2012 to 2018 in China, a multistage dynamic DID model is constructed to systematically explore the effect of policy-oriented agricultural insurance on green agricultural development, primarily to clarify the specific mechanism of the effect. The results show that agricultural carbon emission is increasing year by year and the implementation of policy-oriented agricultural insurance has a significant positive impact on reducing agricultural carbon emissions. We further find that the promotion effect of agricultural insurance on carbon emission reduction increases with the expansion of agricultural technicians and the decrease of agricultural chemical utilization. In terms of spatial heterogeneity, the impact of the policy-oriented agricultural insurance on reducing agricultural carbon emissions is stronger in central and western regions than in eastern regions. At the same time, this paper provides operational suggestions for low-carbon agricultural development and the formulation of relevant macroeconomic agricultural policies. Our findings support the positive effect of policy-oriented agricultural insurance and provide significant policy implications for agricultural carbon emission reduction and control actions in China and other countries. [ABSTRACT FROM AUTHOR]

Making environmental regulation (ER) in line with local development reality is the key to promoting regional coordinated development. Current research mainly concentrates on the overall effect of ER or the policy effect of some specific regulations. In reality, the ERs are not always one-size-fits-all, and the evaluation of the biased ER's effect is mostly overlooked. This paper takes the biased energy-saving policy (BESP) of 13th Five Year Plan as an example and identifies the impact of the BESP on enterprises' total factor productivity (TFP) based on a Difference-in-Difference model. Our results suggest that the BESP has significant promotion on the enterprises' TFP. When considering the heterogeneity of enterprise and industrial characteristics, it can be found that large-scale, state-owned enterprises as well as enterprises with capital-intensive show more significant promotion effect from the BESP. Moreover, enterprises in high-energy consumption and low-tech industries also reveal more significant policy effect. Further, the results of the mechanism analysis indicate that the BESP promotes the enterprises' TFP mainly through enhancing technical-focused investment preference and narrowing technical gap within the industry. Our research fills the gap in the study of biased ER, which provides a new orientation for realizing the carbon neutrality goal. [ABSTRACT FROM AUTHOR]

To effectively solve the environmental pollution caused by greenhouse gases, countries around the world have successively set up carbon emission trading markets in recent years. At present, China's carbon trading market is only in its infancy, making various mechanisms and policies insufficient. As the core issue of carbon trading market, the fluctuations of carbon trading price are related to the investment decisions of market participants, the formulation of enterprise production and operation plans, and the realization of global emission reduction targets. Therefore, it is of great practical significance to study the influencing factors of carbon price fluctuations and to predict future carbon prices. In this paper, we take the carbon trading week data of the Beijing carbon trading market in China from May to September 2020 as the research object. Firstly, the gray correlation technique is employed to measure the rationality of the selected factors affecting carbon price fluctuations. Secondly, through the principal component analysis method to analyze the various influencing factors, it is found that energy prices and macroeconomic development are the main factors affecting carbon prices, and weather conditions will also cause carbon price fluctuations. Then, four different models are proposed to predict carbon trading prices, and the forecast results are evaluated through the performance evaluation index system. The results show that the GA-ELM model has the best prognosis effect. Finally, according to the analysis results, it provides a useful theoretical reference for carbon market decision makers and participants. [ABSTRACT FROM AUTHOR]

Efficiency Power Plant (EPP) is a package of power demand side projects with zero emission, zero pollution and other advantages. In the process of implementing EPP, enterprises not only save electricity, but also reduce carbon emissions, while meanwhile increase the cost of R&D and equipment. Therefore, it is very necessary to study relationships between carbon quota and EPP implementation decision. In this paper, we build the profit functions of three different types of enterprises implementing EPP and analyze the relationship of main parameters, such as the probability of implement EPP, electricity saving, income, cost and carbon quota, and refine 11 relative results. At last we use 'Maple' software to verify the validity of the results, and carry out an empirical analysis of a case in China to prove the practicability of the relationship analysis results. The conclusion shows that under the carbon quota trading environment, enterprises should tend to implement EPP, and increase investment in R&D and acquisition of EPP, and need adjust the intensity of implementing EPP according to the change of carbon quota, unit carbon quota income, to obtain higher income. [ABSTRACT FROM AUTHOR]

With the continuous emission of greenhouse gases, the carbon trading market has become a powerful weapon to contain it. It is indispensable to analyze the carbon price of China that acts as the largest emitter of carbon dioxide worldwide. Therefore, this paper proposes an innovative hybrid carbon price forecasting model that incorporates fast ensemble empirical mode decomposition (FEEMD) and extreme learning machine optimized by particle swarm optimization (PSO-ELM) with external and internal influencing factors considered. The original carbon price series are disassembled into several intrinsic mode functions (IMFs) and one residual via FEEMD. The PSO-ELM is then employed to forecast the sub-series. It's remarkable that the inputs of the PSO-ELM model are divided into external and internal influencing factors. Factor analysis is used to extract potential factors from energy prices, macroeconomics and other influencing factors associated with the original carbon price as external influencing factors, and the partial autocorrelation function (PACF) is exploited to select internal influencing factors. A case study in Hubei Province, China shows that the proposed carbon price forecasting model is superior to the contrast models in terms of the smallest prediction error (MAE = 0.1274 yuan, MAPE = 0.8368%) and the strongest stability (RMSE = 0.0116 yuan). And the forecasting results demonstrate that the developed model with external and internal influencing factors considered can highly improve carbon price prediction performance and have potential in a wider range of carbon price forecasting. In addition, accurate carbon price forecasting can help the government realize macro control and the investors fulfill risk minimization. [ABSTRACT FROM AUTHOR]

The regional distribution and driving factors of total carbon emissions have been the focus of considerable research. However, carbon intensity rather than total carbon emissions has been selected as the emissions reduction index in China. The Chinese government has committed to reducing carbon intensity by 60-65% from 2005 levels. Currently, limited academic attention has been given to the regional distribution and driving factors of carbon intensity. To explore the means of achieving the carbon intensity target in China, Gini coefficients were employed in this paper to investigate regional differences in carbon intensity across 30 provinces from 1995 to 2014. Moreover, the FGLS (feasible generalized least squares) method was applied to identify the key influencing factors of carbon intensity at the national and three regional levels. The results indicate that: 1. Chinese inter-provincial Gini coefficients of carbon intensity have increased steadily in recent years, which indicates that the difference in carbon intensity between provinces in China has widened. 2. Economic growth, foreign direct investment, and trade openness were negatively correlated with carbon intensity. Conversely, coal consumption, industrial proportion, and urbanization were positively correlated with carbon intensity. Moreover, urbanization has proven to be the most important factor affecting China's carbon intensity. 3. The dominant cause of carbon intensity varies by region. In particular, the dominant cause of carbon intensity in low- and medium-level regions is urbanization. However, the dominant cause of carbon intensity in high-level regions is coal consumption. 4. Based on these empirical findings, policy recommendations to reduce carbon intensity were proposed. In summary, the improvement of urbanization quality in both low- and medium-level regions is urgently needed. However, optimizing the energy structure is essential to carbon intensity reduction in high-level regions. [ABSTRACT FROM AUTHOR]