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The green fiscal policy stands as a potent catalyst for fostering the green and low-carbon transition, with aims to garner multifaceted social, economic, and ecological gains. However, empirical evidence elucidating the impacts of green fiscal policies on firm energy performance remains scarce. Based on a unique firm-level dataset from 2008 to 2016, this paper utilizes the staggered difference-in-differences strategy to examine the energy-saving benefits of China's energy saving and emission reduction (ESER) fiscal policy. The results indicate that the ESER fiscal policy effectively enhances firm energy performance, supported by various robustness checks. Mechanism analysis reveals that technology innovation and energy consumption restructuring are significant channels through which the ESER fiscal policy improves firm energy performance. Moreover, the ESER fiscal policy contributes more remarkably to the energy performance of large-scale firms and non-resource-based urban firms. This study highlights the essential policy insights for further optimizing green fiscal policies to facilitate the upgrading of firm energy consumption. [ABSTRACT FROM AUTHOR]

With environmental problems have become a global concern, large number of environmental policies have been enacted worldwide. Though improving personal willingness to pay (WTP) for environmental protection is key objective of environmental policies, existing literature overlook the micro relevance of environmental policies and lacks the exploration of the relationship between environmental policies and WTP and related mechanisms. To fill the research gap, this study took China's energy-related environmental policies as a typical example, drew wisdom from policy feedback theory to develop a theoretical framework, and used CGSS 2018 data to analyse interpretative effect and resource effect of related policies on WTP. The empirical results show that policies have a positive impact on WTP of approximately ¥1.19, but this impact comes with complex mechanisms. On the one hand, policies increase energy knowledge, thus improving WTP (interpretative effect); on the other hand, policies alleviate energy poverty, which further reduces WTP (resource effect). The positive interpretative effect is larger than negative resource effect. The robustness of findings is confirmed by adjusting the samples, measurements and models. This study is among the first to examine the influence mechanism of environmental policies on personal WTP through the lenses of interpretative and resource effects and reveals an unexpected consequence of policies. Accordingly, this study provides policy recommendations for environmental governance. [ABSTRACT FROM AUTHOR]

Forecasting carbon prices has important implications for the formulation of environmental and energy policies as well as for the investment and management of workers in related industries. This paper proposes a novel multi-index nonlinear ensemble framework based on carbon price trading data and technical index data, which combines extreme gradient boost (XGBoost), K-means clustering algorithm based on elbow method (KCE), bidirectional long and short-term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU). First, the XGBoost method is used to filter the raw data set to determine the input variables, and they were then divided into groups using the KCE method. Then, the BiLSTM method is used as the predictor for forecasting the sub model. Finally, BiGRU is applied to nonlinear integration. The developed prediction framework is used to predict China's regional carbon price to evaluate its performance. Experimental results show that the prediction performance of the framework in all target carbon markets is better than the benchmark model, and it is an effective tool for carbon market. [ABSTRACT FROM AUTHOR]

The response of energy firms to green credit policy is of great significance, which is related to the emission reduction effect of green finance and transformation of energy firms. This paper analyzes the impact of green credit policy on the growth of energy firms based on the data of Chinese listed companies from 2009 to 2019. The empirical results show that green credit policy has significantly promoted the growth of energy firms. Further research shows that green credit policy promoted the growth of energy firms by reducing financing costs and promoting green innovation. Besides, the owned firms, big-scale firms and firms in central and eastern China are more susceptible to the impacts of the green credit policy. This study is relevant to the implementation of green credit policies and the promotion of the development and transformation of energy firms. [ABSTRACT FROM AUTHOR]

The lower price of energy leads to higher coal consumption in China. The idea of an "environment-for-trade policy" could be used to achieve an international competitive advantage, which, in turn, has important implications. To address the issue, we develop properties to examine the link between the low price of energy and strategic environmental policy in China and investigate the choice of policy instruments in a strategic environmental policy model with vertical contracts. In addition, to contribute to the literature on strategic environmental policy, this paper also develops properties to investigate different choices of instruments for the environmental policy and includes the degree of energy marketization for the wholesale price in the study. To do so, we assume that the wholesale price of the polluting input increases with the market price. By using this assumption, this paper analyzes the effects of two instruments of the environmental policy on social welfare and concludes that there is no reason to expect both downstream and upstream firms to establish a high wholesale price. Due to the low level of marketization, when the government selects an emission tax as the policy instrument, the optimal tax rates should be higher than the marginal damage of emissions. However, the optimal resource tax is uncertain when its effect on environmental damage is taken into account. In other words, the resource tax is ineffective as a policy instrument. Our results can be used to draw some practical policies for countries to use their energy effectively. To promote energy sustainability, governments should liberate resource prices and reform the system to get efficient environmental policies. [ABSTRACT FROM AUTHOR]

Given the global inclination of economies toward green solutions, the current study aims to explore the association between green energy transition, adjusted net saving, energy intensity, and carbon dioxide emission in China. To achieve this, a novel approach utilizing Wavelet analysis is employed to examine the dynamic association among these variables from 1979 to 2020. The analysis utilized continuous wavelet, wavelet coherence, and partial and multiple wavelet coherence techniques, offering a comprehensive understanding beyond the limitations of prior research by investigating the relationships over various time frames. The findings from the wavelet coherence analysis yielded intriguing insights, indicating that an increase in energy transition is likely to result in higher energy intensity and increased CO2 emissions. Surprisingly, the study observed a negative impact of adjusted savings on energy transition in the medium run, whereas in the long term, an increase in adjusted net savings was associated with higher carbon emissions in the Chinese economy. This highlights the intricate interplay between energy transition, adjusted savings, and environmental impacts, with different time scales playing a significant role in shaping these connections. Policymakers should carefully consider these dynamics to formulate effective strategies for sustainable development and carbon emission reduction in China. Furthermore, employing partial and multiple coherence analysis, the outcomes has demonstrated a strong interconnected correlations between energy intensity, green energy transition, CO2 emissions, and adjusted net savings across short-run, medium-run, and long-run time frequencies. Overall, the study concludes the positive link between energy and carbon emissions in China, while the effectiveness of the green energy transition remains vulnerable. The findings offer valuable insights for informed decision-making and policy implications, facilitating the adoption of appropriate measures to promote a greener and more sustainable future in China. • Study explores green energy transition, net saving, energy intensity, and CO 2 in China. • Advanced time-frequency based Wavelet analysis is used to analyze asymmetric relationships. • Energy transition is found to have positive link with CO 2 and energy intensity. • Net adjusted saving have positive effect on CO2 but negative on energy transition. [ABSTRACT FROM AUTHOR]

The environmental and socioeconomic considerations of energy production have become crucial because of the increasingly complex relationship between energy and the environment. This study aims to develop possible mechanisms for perspectives on energy policy and the environment by exploring the mediating role of renewable energy patents. Non-radial data envelopment analysis and panel data models are applied using the panel data from 2010 to 2017 from 30 Chinese provinces. The results show an overall improvement in the environmental performance index (EPI) of China's provinces, but the average EPI is still relatively weak, with an average value between 0.44 and 0.52, which is far below the optimal value 1.. Furthermore, the econometric model offers evidence that provincial renewable energy and emission reduction policies positively impact the enhancement of EPI. The findings have several implications for energy and environmental policies. • We assess energy policy paradox on environmental performance. • We analyze renewable and emission reduction polices on different levels. • We investigate renewable energy patents impact on carbon emissions reduction. [ABSTRACT FROM AUTHOR]

Abstract: The better to explore the regional characteristics of inter-provincial CO2 emissions and the rational distribution of the reduction of emission intensity reduction in China, this paper proposes an improved PSO-FCM clustering algorithm. This method can obtain the optimal cluster number and membership grade values by utilizing the global capacity of Particle Swarm Optimization (PSO) on Fuzzy C-means (FCM). The clustering results of CO2 emissions indicate that the 30 provinces of China are divided into five clusters and each has its own significant characteristics. Compared with other clustering methods, the results of PSO-FCM are more explanatory. The most important indicators affecting regional emission characteristics are CO2 emission intensity and per capita emissions, whereas CO2 emission per unit of energy is not obvious in clustering. Furthermore, some policy recommendations on setting emission reduction targets according to the emission characteristics of different clusters are made. [Copyright &y& Elsevier]

Abstract: This study aims to examine how mitigating CO2 through a carbon tax might affect the development goals of narrowing urban–rural gap and improving people’s living standard. In this study, the China Energy and Environmental Policy Analysis (CEEPA) model, a recursive dynamic computable general equilibrium model, was employed to simulate taxing carbon in China. Different carbon tax schemes were designed and their impacts on household disposable income, household welfare, economic growth, and CO2 emissions were compared. Results show that, given the current social security system that obviously favors urban households and the current investment-driven economic growth pattern, without complementary measures for protecting households, a carbon tax will not only widen the urban–rural gap, but also reduce the living standards of both urban and rural households. The negative impacts caused by carbon tax will enlarge over time. An ideal solution, no matter under an emission intensity goal or a total amount control goal, is to reduce indirect tax with carbon tax revenue, whilst increase the share rural households obtain in government transfers. [Copyright &y& Elsevier]

Optimizing energy consumption structure (ECS) is the key to reduce pollution emissions and develop a green economy. Understanding the effect of each influencing factor on ECS is conducive to providing theoretical reference for the formulation of energy and environmental policies, howbeit it lacks studies about the main drivers of national and regional ECS in China. With the consideration of increasingly standardized formal environmental regulation (FER), an extended LMDI model is constructed to decompose the national and regional ECS from 1998 to 2017 into ten influencing factors. The ultimate goals of this concept are to explore the dominant factors of national and regional ECS at different time stages by employing temporal decomposition model, and to investigate the primary drivers that cause the disparity between provincial ECS and national average ECS by applying spatial decomposition model. The results reveal that the effect of FER, the reciprocal of industrial pollution control investment per unit GDP and economic development are the main factors leading to the change of national and regional ECS, specifically FER has a stronger effect on resource-based regions. Also, R&D intensity and R&D efficiency are important factors for ECS change. Energy efficiency and Per capita energy production are the leading factors of ECS disparity between 30 provinces and the national average. What's more, FER and R&D factors play important roles. Policy suggestions are put forward according to the results. Image 1 • Energy efficiency acts as leading force to increase ECS. • R&D intensity and R&D efficiency are important factors for ECS. • FER has a stronger effect on resource-based regions. • Policy- and development-oriented factors should be considered in policy formulation. [ABSTRACT FROM AUTHOR]

This paper investigates energy use efficiency at the province level in China using the stochastic frontier panel data model approach. The stochastic frontier model is a parametric model which allows for the modeling of the relationship between energy use and its determinants using different control variables. The main control variables in this paper are energy policy and environmental and regulatory variables. This paper uses province level data from all provinces in China for the period 2010–2017. Three different models are estimated accounting for the panel nature of the data; province-specific heterogeneity and province-specific energy inefficiency effects are separated. The models differ because of their underlying assumptions, but they also complement each other. The paper also explains the degree of inefficiency in energy use by its possible determinants, including those related to the public energy policy and environmental regulations. This research supplements existing research from the perspective of energy policy and regional heterogeneity. The paper identifies potential areas for improving energy efficiency in the western and northeastern regions of China. Its findings provide new empirical evidence for estimating and evaluating China's energy efficiency and a transition to cleaner energy sources and production. [ABSTRACT FROM AUTHOR]