Can environmental tax reform promote carbon abatement of resource-based cities? Evidence from a quasi-natural experiment in China

Impact of Environmental Protection Tax on carbon intensity in China

In the context of increasingly severe global climate change, finding effective carbon emission reduction strategies has become key to mitigating climate change. Environmental Protection Tax (EPT), as a widely recognized method, effectively promotes climate change mitigation by encouraging emission reduction behaviors and promoting the application of clean technologies. Based on data from 282 cities in China, this paper takes the official implementation of the EPT in 2018 as the policy impact and the cities with increased tax rates for air taxable pollutants as the treatment group and uses DID model to systematically demonstrate the relationship between the implementation of the EPT and carbon intensity (CI) and further explores the possible pollutant emissions and green innovation mediating effects. The findings show that (1) the implementation of EPT can effectively reduce CI by about 4.75%, and this conclusion still holds after considering the robustness of variable selection bias, elimination of other normal effects, policy setting time bias, and self-selection bias. (2) The implementation of EPT can reduce CI by reducing pollutant emissions and improving the level of green innovation. (3) There is obvious regional heterogeneity in the carbon reduction effect of EPT, and the implementation of EPT has a more significant effect on CI in medium-tax areas, low environmental concern areas, general cities, and eastern regions. This paper not only provides a new analytical perspective for systematically understanding the carbon emission reduction effect of EPT but also provides policy insights for promoting regional green transformation and advancing carbon peak carbon neutralization.

Has environmental protection tax reform promoted green transformation of enterprises? Evidence from China

Facing the increasingly stringent constraints of resources and the environment, the green transformation of enterprises is imperative. This study selects A-share listed companies in Shanghai and Shenzhen from 2014 to 2021 as samples, using the difference-in-differences method to examine the impact of the environmental protection tax reform (EPTR) on the green transformation of enterprises. The results indicate that the EPTR can promote the green transformation of enterprises, achieving this through three channels: raising the cost of pollution, strengthening the rigidity of law enforcement, and breaking the collusion between the government and enterprises. Notably, this promotional effect is more significant in non-state-owned enterprises, companies in the eastern and western regions, firms with low financing constraints, and those with high media attention. Further analysis shows that the EPTR has a positive impact on the green total factor productivity (GTFP) of enterprises, which implies that enterprises are not only proactively pushing for a green transformation at the strategic level but also taking practical actions. This study responds to the problem of the greening of tax system to promote the green development of enterprises from two aspects of enterprise strategic implementation and productivity and explores the impact mechanism from the perspective of institutional logic. It enriches the research on the effectiveness of the EPTR at the micro-level and broadens the research perspective on the impact mechanisms of environmental regulation. The findings of this study provide references for further optimising relevant policies and regulations and also offer insights for other countries and regions seeking sustainable development.

Facilitating or inhibiting? The impact of environmental information disclosure on enterprise investment value

Environmental protection, which is beneficial for the present and the future, has become a global consensus, and environmental information disclosure (EID) is an effective way to realize and fulfill enterprise environmental responsibility. Although some scholars have studied the impact of EID on firms, there is less empirical evidence on the impact of EID on investors. In this study, we examine the impact of EID on enterprise investment value based on signaling theory using a time-varying difference-in-differences model and extract two channels of this effect. The study shows that the implementation of EID helps to enhance the value of enterprise investment. This enhancement will vary according to the location, the industry pollution type, and the nature of the enterprise: EID has a remarkable enhancement effect on the investment value of the eastern region, heavily polluted enterprises, and non-state-owned enterprises. To investigate the channel of EID's effect on enterprise investment value, we use the moderating effect model to analyze and find that enterprises with low tax ratios and small financing constraints can significantly enhance the effect of EID on investment value.

Central vertical regulation and urban environment-biased technological progress: evidence from China

Technological progress in favor of cleaner production is the key to achieving low-carbon development in China. The Ambient Air Quality Standard (AAQS) issued by the Ministry of Environmental Protection (MEP) in 2012 was an essential policy for the central government to implement vertical environmental regulation. Therefore, based on the city-level panel data, this paper examines the impact of the central vertical regulation on urban environment-biased technological progress using the difference-in-differences method. The results show that central vertical regulation can significantly promote urban environment-friendly technological progress. Heterogeneity analysis shows that the driving effect of the central vertical regulation on urban environment-friendly technological progress is more obvious in the eastern regions, non-resource-based cities, large cities, and high-grade cities. Moreover, the channel analysis shows that the central vertical regulation mainly boosts the urban environmental technology progress toward cleaner production by strengthening government environmental governance, raising public environmental concern, and improving energy structure. The findings provide policy implications for evaluating the effectiveness of macro-environmental policy and promoting green sustainable development.

Can environmental tax improve the environmental investment? Evidence from a quasi-natural experiment in China

The implementation of the Environmental Tax Law is a milestone in promoting China's green tax reform. However, the existence literature has lacked attention to whether it leads enterprises to invest in green environmental protection. To examine the Environmental Tax Law effects and mechanism on the environmental investment of heavy-polluted enterprises, this study used the data of heavy-polluted enterprises listed on the A-share market from 2012 to 2020 and regarded the Environmental Tax Law as a quasi-natural experiment to employ a difference-in-differences model. We found that environmental tax improves the green environmental investment of heavy-polluted enterprises, reflecting the guiding role of policy on enterprise investment allocation. Heterogeneity was found, and the promotion effect of environmental tax reform on enterprise environmental investment is more significant in non-nation-owned, central-western regions, and small-scale enterprises. Further analysis believed that market competition, as an external mechanism, helps strengthen environmental tax reform's implementation effect. The findings of this paper provide a new proof for a comprehensive understanding of the micro-effect of environmental tax reform and provide a reference for the implementation of green development strategies.

Eco-innovations, green growth, and environmental taxes in EU countries: a panel quantile regression approach

The purpose of this study is to examine how environmental taxation, green growth, and eco-innovations contribute to a more sustainable environment. This study examines the influence of green growth, environmental taxes, and eco-innovations on carbon dioxide emissions in 26 environmentally responsive European Union (EU) countries from 2000 to 2020. The analysis was conducted using the second-generation panel unit root test, cross-sectional dependence, panel cointegration, and panel quantile regression. Theoretical and empirical research has demonstrated that both linear and non-linear green growth strategies are effective in reducing CO2 emissions. There is evidence that CO2 emissions can be reduced through the implementation of environmental taxes, eco-innovations, the use of renewable energy sources, and enhanced energy efficiency. In contrast, economic growth has a positive effect on carbon emissions, and its square term verifies the environmental Kuznets curve. Nevertheless, our research findings provide empirical support for the hypothesis that sustainable development contributes to the maintenance of stringent environmental standards. For the sampled countries, the study's findings have significant policy implications. These results encourage governments to prioritize green growth over traditional economic growth and to encourage eco-innovations in renewable energy technology.

Trilemma of capital, urbanization, and renewable energy: contextual evidence from China

In the case of China, there is a need of green energy system to boost growth and environment. However, the current growth of urbanization is putting high pressure on energy system through financial capital. Hence, it is a need to develop such path through renewable energy consumption, capital development, and urbanization to enhance the performance of development and environment. Therefore, this paper contributes to the literature by adding the asymmetries between renewable energy, urbanization, economic growth, and capital investment by covering the period from 1970 to 2021. For this purpose, we apply the nonlinear autoregressive distributed lag model to find the nonlinearities between the variables under study. The findings confirm the asymmetric short- and long-term relationships between the variables. In this sense, capitalization shows the short- and long-term asymmetric impacts on renewable energy consumption. In addition, urbanization and economic growth have long-term asymmetric and positive impacts on renewable energy consumption. At last, this paper provides applicable and practical policy implications for China.

Simulating and Comparing CO2/CH4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks

Separating carbon dioxide (CO₂) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO₂ capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO₂ separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO₂ capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO₂/CH₄ selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO₂/CH₄ selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO₂/CH₄ selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).

The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance

The development of a country is inseparable from the material guarantee mainly based on energy, but energy is limited, which may restrict the sustainable development of the country. It is very necessary to accelerate the adoption of programs aimed at switching non-renewable energy sources to ones that are, and giving priority to improving renewable energy consumption and storage capabilities. From the experience of the G7 economies, the development of renewable energy (RE) is inevitable and urgent. The China Banking Regulatory Commission has recently issued a number of directives, such as the "Directives for Green Credit" and "Instructions for Granting Credit to Support Energy Conservation and Emission Reduction," to help businesses that use "renewable energy expand". This article firstly discussed the definition of the "green institutional environment" (GIE) and the construction of the index system. Then, on the basis of clarifying the relationship between the GIE, and RE investment theory, a semi-parametric regression model was constructed to empirically analyze the mode and effect of the GIE. Considering the balance between improving model accuracy and reducing computational complexity, the number of hidden nodes opted in this study is 300 so as to lower the time needed to predict the model. Finally, from the perspective of enterprise scale, the level of GIE played a significant role in promoting RE investment in small and medium-sized enterprises, with a coefficient of 1.8276, while the impact on RE investment in large enterprises had not passed the significance test. Based on the conclusions, the government should focus on building a GIE dominated by green regulatory systems, supplemented by green disclosure and supervision systems, and green accounting systems, and should make reasonable plans for releasing various policy directives. At the same time, while offering full play to the guiding role of the policy, its rationality should also be paid attention to, and the excessive implementation of the policy should be avoided, so that an orderly, and good GIE can be created.

Banking sectors and carbon neutrality goals: mediating concern of financial inclusion

Since industrialization, GHGs have steadily grown, and climate change threatens human civilization. The Chinese government actively engages in the administration of the global environment and has suggested that carbon neutrality be attained by 2060. Regional communities must understand their current carbon neutrality status and objectively design a course to attain carbon neutrality due to significant regional development disparities. This research uses a GMM model in order to investigate the effect of the banking sector and financial inclusion on carbon neutrality for 30 provinces in China for the period of 2000-2020. The following are the key conclusions: (1) clean and efficient energy use, primarily reflected by carbon emissions intensity, carbon dioxide emissions per capita, and coal expenditure per capita, had the most significant influence on attaining carbon neutrality. (2) In terms of energy, economics, and environmental considerations, water consumption per capita, the volume of technology distribution, and carbon pollution intensity were the elements that had the most significant impact on carbon neutrality. (3) The provinces might be categorized into three groups depending on their ability to become carbon neutrality, with developed economies having an easier time doing so than resource-dependent provinces. Financial inclusion should also be increased in order to achieve long-term sustainability of the environment. The findings stand up well to both immediate and long-term policy consequences. The sustainable development goals (SDGs) of the United Nations (UN) are supported by this research.

Assessing the effect of the improvement of environmental damage compensation legal system and green finance project on the re-establishment of the ecological environment

What are the relationships among environmental regulations, green finance, and environmental damages in countries? Existing literature supports the impact of green finance or green innovation on environmental quality, but rare studies query the cointegration among other core variables. We thus utilize the yearly data of 25 Chinese provinces from 2003 to 2021 to empirically examine the relationships among access to clean energy and technology, environmental regulation, renewable green investment, subsidy on green energy, and green finance index in environmental damage compensation via an augmented mean group (AMG) and other estimators. However, the current empirical research also investigates the individual linkage of green finance components with explained variables. Overall, this study confirms the existence of cointegration relationships among these variables. Moreover, the results of AMG suggest that access to clean fuels and technology, environmental regulations, and green finance can inversely affect the explained variable in the long term. Furthermore, environmental regulations and renewable green investment positively affect environmental damages, while a separate proxy of green finance also negatively affects explained variables in the selected provinces with better environmental performance. Our empirical findings offer important policy implications for overall emerging economies to promote subsidies, environmental regulations, and green finance to improve environmental damages compensation.

China's energy policy and sustainable energy transition for sustainable development: green investment in renewable technological paradigm

It is generally accepted that China is a significant cause of global warming and other climate change consequences. This paper applies panel cointegration tests and autoregressive distributed lag (ARDL) techniques to investigate the interactions among energy policy, technological innovation, economic development, trade openness, and sustainable development using panel data from China from 1990 to 2020. Results explain that renewable energy policy and technology innovation are negatively associated with sustainable development. However, research shows that energy use significantly increases both short-term and long-term environmental damage. The findings show that economic growth has a lasting impact on the environment by distorting it. The findings recommend that politicians and government officials hold the key to attaining a green and clean environment by focusing on developing the proper energy policy mix, urban planning, and pollution prevention without compromising economic growth.

A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning

This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional gas emission prediction models are neither accurate nor universally applicable. Through analysis, this paper identified 12 factors that affected gas emissions. A total of 30 groups of typical data for gas outflow were standardized, after which a full subset regression feature selection method was used to categorize 12 influencing factors into different regular patterns and select 18 feature parameter sets. Meanwhile, based on nuclear principal component analysis (KPCA), an optimized gas emission prediction model was constructed where the dimensionality of the original data was reduced. An optimized algorithm set was constructed based on the hybrid kernel extreme learning machine (HKELM) and the least squares support vector machine (LSSVM). The performance of feature parameters adopted in the prediction algorithm was evaluated according to certain metrics. By comparing the results of different sets, the final prediction sequence could be obtained, and a model that was composed of the optimal feature parameters was applied to the optimal machine learning algorithm. The results showed that the HKELM outperformed LSSVM in prediction accuracy, running speed, and stability. The root meant square error (RMSE) for the final prediction sequence was 0.22865, the determination coefficient (R2) was 0.99395, the mean absolute error (MAE) was 0.20306, and the mean absolute percentage error (MAPE) was 1.0595%. Every index of accuracy evaluation performed well and the constructed prediction model had high-prediction accuracy and a wide application.

Control-Centric Data Classification Technique for Emission Control in Industrial Manufacturing

Artificial intelligence-based hardware devices are deployed in manufacturing units and industries for emission gas monitoring and control. The data obtained from the intelligent hardware are analyzed at different stages for standard emissions and carbon control. This research article proposes a control-centric data classification technique (CDCT) for analyzing as well as controlling pollution-causing emissions from manufacturing units. The gas and emission monitoring AI hardware observe the intensity, emission rate, and composition in different manufacturing intervals. The observed data are used for classifying its adverse impact on the environment, and as a result industry-adhered control regulations are recommended. The classifications are performed using deep neural network analysis over the observed data. The deep learning network classifies the data according to the environmental effect and harmful intensity factor. The learning process is segregated into classifications and analysis, where the analysis is performed using previous emission regulations and manufacturing guidelines. The intensity and hazardous components levels in the emissions are updated after the learning process for recommending severe lookups over the varying manufacturing intervals.

Impact of digital economy on ecological resilience of resource-based cities: spatial spillover and mechanism

Resource-based cities, which are widely distributed in China, contribute significantly to China's sustainable development. The digital economy and the construction of ecological civilization are central issues in the sustainable development of resource-based cities, and it is necessary to analyze the impact of the digital economy on the ecological resilience of resource-based cities. Thus, this paper measures ecological resilience of 117 resource-based cities from 2011 to 2020 using the entropy weight TOPSIS (technique for order preference by similarity to ideal solution; see Table 1 for a list of acronyms) method and empirically investigates the impact and mechanism of digital economy on ecological resilience using the spatial Durbin model (SDM) and intermediary effect model. The results show that the ecological resilience of resource-based cities has a certain upward trend, with a stepwise distribution pattern from east to west. There is a significant positive correlation between ecological resilience of resource-based cities, showing the phenomenon of club convergence which is primarily dominated by high-high (H-H) and low-low (L-L). The digital economy has a significant spatial spillover effect, which promotes ecological resilience of resource-based cities in the local and adjacent regions. A mechanism analysis reveals that technological innovation and industrial structure are mediators between digital economy and ecological resilience of resource-based cities, with significant heterogeneity in region and growth cycle. Among them, the intermediary role of technological innovation is stronger. Following the above findings, this paper proposes policy suggestions related to digital economy evolution and ecological resilience enhancement. This paper further enriches the literature on the ecological resilience and provides a theoretical basis for government policy-making.