Study on the Emission Reduction Effect and Spatial Difference of Carbon Emission Trading Policy in China

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

China is entering a new period characterized by reaching peak and carbon neutralization, and environmental taxes are increasingly crucial for breaking the "carbon curse" of resource-based cities. Accordingly, using the implementation of China's Environmental Protection Tax Law (EPT Law) as a quasi-natural experiment, this study utilizes the DID model to assess this environmental tax reform's effect in terms of reducing carbon emissions. The research results are as follows: (1) The environmental tax reform (ETR) reduced the intensity of carbon emissions; it additionally promoted reducing total carbon emissions from resource-based cities. (2) The carbon abatement effect can also be achieved by upgrading industrial structures and improving innovation in the area of green technology. (3) The ETR has impacted carbon abatement in resource-based cities more significantly in China's eastern region than in the central or western regions. In contrast, it had less effect on resource-based cities in the regenerative stage than on cities in other stages. (4) The spatial spillover effect of the ETR was significantly positive, aggravating the level of carbon emissions in neighboring cities. Thus, the "pollution haven hypothesis" was tested. Overall, this study deepens the knowledge of ETR and carbon emissions and provides theoretical support and policy suggestions for supporting resource-based cities in a green transformation.

The effect of geopolitical risk on carbon emissions: influence mechanisms and heterogeneity analyzed using evidence from China

To meet the goals of reducing adverse effects, continuing economic transformation, and achieving sustainable development, it is necessary to understand the impact mechanism and heterogeneous effects of geopolitical risk on carbon emissions. Using panel data from 30 provinces in China gathered between 2003 and 2019, we show that (1) geopolitical risk significantly contributes to the growth of carbon emissions, as does non-renewable energy consumption, trade, and economic growth, but that technological progress, industrial structure upgrading, and marketization inhibit the growth of carbon emissions; (2) geopolitical risk inhibits carbon emissions by suppressing non-renewable energy consumption and trade, and promoting technological progress; and (3) geopolitical risk has heterogeneous effects on carbon emissions in different quartiles. In the lower quartiles (i.e., groups with lower emission levels), geopolitical risk suppresses carbon emissions, while in higher quartiles (i.e., groups with higher emission levels), geopolitical risk promotes carbon emissions. As growing geopolitical risk and carbon emissions are now common problems for all countries, this study serves as a valuable reference not only for China, but for every member of the global community seeking to mitigate geopolitical risk shocks and achieve carbon emission reduction targets.

Can new energy demonstration city policy reduce carbon emissions? A quasi-natural experiment from China

Against achieving carbon peaking by 2030 and carbon neutrality by 2060 context in China, the new energy demonstration city policy (NEDCP) has a crucial function to perform in promoting resource utilization efficiency, building the green development policy system, and facilitating carbon emission reduction. However, existing research has rarely investigated the contribution of NEDCP on carbon reduction. To investigate the policy effect of NEDCP, the differences-in-differences (DID) model is introduced to quantify the influence of NEDCP on carbon reduction, taking a statistical sample of 285 Chinese cities over the period 2005-2017 on the basis of exploring the intrinsic mechanism of NEDCP on carbon emissions. The statistical results reveal that NEDCP significantly inhibits carbon emissions. NEDCP's dampening impact on carbon reduction is more pronounced in the eastern area but not in other areas. City size and resource endowment heterogeneity results suggest that NEDCP significantly inhibits the output of carbon emissions in non-resource-based and large cities but insignificantly in resource-based and small- and medium-sized cities. Finally, we conclude that policy-makers should not only broaden the scope of NEDCP implementation continuously but also design relevant policy combination tools following the basic characteristics of each city to provide institutional guarantees for achieving carbon emission reduction.

Does China's Carbon Trading Pilot Policy Reduce Carbon Emissions? Empirical Analysis from 285 Cities

This article studies the influence of the Carbon Trading Pilot Policy (CTPP) on carbon emissions by constructing the balanced panel data from 2003 to 2020 for 285 cities in China above the prefecture level. Difference-in-Difference (DID) method is used to test the influence and the mechanism. (1) The findings suggested that CTPP has dramatically reduced China's carbon emissions by 6.21%. The parallel trend test shows that the premise of DID is reliable. (2) A variety of robustness tests, such as the instrumental variable method for endogeneity, Propensity Score Matching (PSM) for sample selection bias, variable substitution, time-bandwidth change, and exclusion of policy intervention, show that the conclusion is still robust. (3) The mediation mechanism test indicates that CTPP can promote the reduction in carbon emissions by promoting Green Consumption Transformation (GCT), improving Ecological Efficiency (EE), and promoting Industrial Structure Upgrading (ISU). GCT contributes the most, followed by EE and ISU. (4) The analysis of the heterogeneity reveals that CTPP has a greater effect on carbon emission reduction in central and peripheral cities in China. This study provides policy implications for China and similar developing countries in the face of carbon reduction.

Identifying carbon emission characteristics and carbon peak in China based on the perspective of regional clusters

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region.

Digital Economy and Environmental Quality: Insights from the Spatial Durbin Model

Recent developments in attaining carbon peaks and achieving carbon neutrality have had enormous effects on the world economy. Digitalization has been considered a viable way to curtail carbon emissions (CE) and promote sustainable economic development, but scant empirical studies investigate the link between digitalization and CE. In this context, this study constructs the digitalization index using the entropy value method and spatial Markov chain, and the spatial Durbin model is employed to analyze its impact mechanism and influence on urban CE in 265 prefecture-level cities and municipalities in China from 2011 to 2017. The results indicate that: (1) The overall development level of the digital economy (DE) posed a significant spatial effect on urban environmental pollution. However, the effect varies according to the different neighborhood backgrounds. (2) The DE impedes urban environmental deterioration directly and indirectly through the channels of industrial structure, inclusive finance, and urbanization. (3) The development of the DE significantly reduces pollution in cities belonging to urban agglomerations, while the development of the DE escalates emissions in nonurban agglomeration cities. Finally, based on the results, important policy implications are put forward to improve the environmental quality of cities.

Temporal-Spatial Evolution and Driving Factors of Global Carbon Emission Efficiency

With global warming, the continuous increase of carbon emissions has become a hot topic of global concern. This study took 95 countries around the world as the research object, using the Gini coefficient, spatial autocorrelation, spatial econometric model and other methods to explore temporal and spatial evolution, and spatial agglomeration characteristics from 2009 to 2018. The results are as follows: First, global carbon emission efficiency (CEE) showed an overall upward trend, and the average value fluctuated from 0.3051 in 2009 to 0.3528 in 2018, with an average annual growth rate of 1.63%. Spatially, the areas with higher CEE are mainly located in Western Europe, East Asia, and North America, and the areas with lower values are mainly located in the Middle East, Latin America, and Africa. Second, the Gini coefficient increased from 0.7941 to 0.8094, and regional differences showed a gradually expanding trend. The Moran's I value decreased from 0.2389 to 0.1860, showing a positive fluctuation characteristic. Third, judging from the overall sample and the classified sample, the correlations between the influencing factors and CEE were different in different regions. Scientific and technological innovation, foreign direct investment and CEE in all continents are significantly positively correlated while industrial structure is significantly negatively correlated, and urbanization, economic development level, and informatization show obvious heterogeneity. The research is aimed at strengthening exchanges and cooperation between countries, adjusting industrial structure; implementing emission reduction policies according to local conditions; and providing guidance and reference for improving CEE and mitigating climate change.

Evaluation of Acceptance Capacity of Distributed Generation in Distribution Network Considering Carbon Emission

Under the background of renewable-dominated electric power system construction, the penetration rate of low-carbon and renewable distributed generation (DG) in distribution network is increasing, which has changed the form and operation mode of the distribution network. To deal with the output fluctuation of high penetration DG in the distribution network operations, it is necessary to evaluate the acceptance capacity of DG. The correct evaluation can realize the secure, economic and low-carbon configuration of DG. In this paper, an evaluation method of acceptance capacity of DG in the distribution network considering the carbon emission is proposed. Firstly, a multi-objective evaluation model of acceptance capacity of DG is constructed with the objectives of minimizing carbon emission in the full life cycle, minimizing node voltage deviation and maximizing line capacity margin. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve the model to determine the Pareto optimal solutions of DG configuration. Then, the comprehensive index of acceptance capacity evaluation is obtained based on entropy weight method to decide the optimal compromise solution. Finally, an actual 55-bus distribution network in China is used to verify the effectiveness of the proposed method. The simulation results show that the proposed evaluation method can comprehensively obtain the optimal compromise solution considering the reliability, economy and carbon emission benefits of distribution network operation, which guides the DG configuration in the distribution network.