Search

To attain China's target of carbon neutrality by 2060, the implementation of the Energy Consumption Trading (ECT) system serves as an effective approach to improve energy efficiency and mitigate CO 2 emissions. Nevertheless, it is crucial to consider not only the importance of carbon reduction effect caused by ECT system but also the associated cost. Therefore, this paper applies the spatial difference-in-differences (SDID) model to assess the direct and spillover impacts of the ECT policy on the carbon abatement cost, which is measured by shadow price of CO 2 emissions and calculated using SBM-DEA model. Panel data encompassing 279 Chinese cities from 2010 to 2019 are employed for analysis, with additional insights provided through heterogeneity and mechanism analyses. The results show that ECT policy increases the local carbon abatement cost in pilot cities but decreases the cost of surrounding cities. Meanwhile, such a declination effect in neighboring cities with a spatial attenuation boundary of 1000 km. The heterogeneity analysis suggests that the implementation of ECT policy leads to an increase in the carbon abatement cost in cities located in non-Eastern Coastal regions, cities with abundant natural resources and cities with small populations. The mechanism analysis indicates that the ECT policy implementation facilitates the improvement of green technology in the neighboring cities of the pilot project, consequently lowering the carbon abatement cost. Besides, this policy also results in a decrease in the carbon intensity of the pilot cities. Owing to the U-shaped association between carbon intensity and the carbon abatement cost, the policy implementation stimulates an increase in the cost within the pilot cities. • This paper estimates the carbon abatement cost (MAC) using SBM-DEA method. • The SDID model is used to investigate the impacts of ECT policy on carbon MAC. • We test whether green technology and carbon intensity are mediating variables. • ECT policy increases the carbon MAC in pilot cities through carbon intensity. • ECT policy lowers the carbon MAC of adjacent cities through the green technology. [ABSTRACT FROM AUTHOR]

[Display omitted] • Measure carbon emission inequalities (CEIs) of urban agglomerations under rapid urbanization. • Identify the key factors determining CEIs within the three urban agglomerations. • Account for the differences between adjacent or distant urban agglomerations. • The declining CEI is mainly due to changes in industrial structure and economic development. • CEI in the upper reaches of the YREB increases due to the changes in energy intensity. Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China's Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future. [ABSTRACT FROM AUTHOR]

Carbon lock-in has become a major obstacle to green development in all countries, and carbon unlocking (CUL) is of great significance in accelerating the process of carbon neutrality worldwide. Based on the panel data of 292 prefecture-level cities in China from 2006 to 2020, this paper constructs a PSM-DID model based on machine learning to empirically test the impact of LCCP policy on urban CUL. The regression result shows that the low carbon city pilot (LCCP) policy significantly increases the level of CUL by 1.25 % in the pilot cities, which is still valid after range of robustness tests, and the mechanism analysis shows that green innovation and green habits are important ways for the policy to affect CUL. Based on the generalized random forest (GRF) algorithm, we further find that the policy can increase the CUL level by 0.6 %, and the average treatment effect (ATE) of the sample is concentrated between 0.004 and 0.006, which exhibits obvious heterogeneity. This paper chooses population quality, openness level and industrial structure as heterogeneous factors, which are finally found to have a U-shaped or inverted U-shaped relationship with ATE. This study provides useful insights for China and other countries to break the carbon lock-in and realize green development. • Low-carbon city pilot policy can effectively promote urban carbon unlocking. • Causal forest model is used for causal inference based on generalized random forests algorithm. • The propensity score is calculated by machine learning. • Generalized random forest is used for heterogeneity analysis. • Low-carbon city pilot policy transmits policy effects through green innovation and green habits. [ABSTRACT FROM AUTHOR]

Innovation is crucial for countries to achieve their carbon peak and carbon neutrality goals. To explore how and to what extent innovation contributes to low-carbon development, this study utilizes data from 272 Chinese cities, applying both staggered difference-in-differences and spatial econometric approaches to comprehensively assess the direct and spatial spillover effects of China's innovative city pilot policy on urban low-carbon development. The findings are as follows: First, the innovative city pilot policy has reduced urban carbon intensity by 4.62 % and increased carbon total factor productivity by 3.03 %, with the effect being more significant in larger cities. Second, mechanism analysis indicates that the innovative city pilot policy primarily promotes urban low-carbon development through innovation financing and talent attraction, rather than optimizing industrial structures Third, spatial analysis reveals that the innovative city pilot policy has a low-carbon spillover effect, indicating it can promote low-carbon development in neighboring cities. Finally, this paper proposes policy recommendations for combining innovative city pilot policies with climate policies, focusing on the underlying mechanisms, and exploring cross-regional linkages. [Display omitted] • The direct and indirect effects of China's Innovative City Pilot Policy on low-carbon development are investigated. • The ICPP leads to reduce carbon intensity by 4.62% and increase carbon TFP by 3.03%. • Both mechanisms and heterogeneous effects of ICPP on carbon intensity and carbon TFP are examined. • The ICPP has the potential to propagate the low-carbon development effect to neighboring cities. [ABSTRACT FROM AUTHOR]

• A SD-PLUS-InVEST model is proposed to simulate the WCS. • Driving-feedback mechanism and effect paths of SEES coupling framework are studied. • The policy balance is analyzed and the contribution to the SDGs is quantified. Wetlands are an important part of the global carbon cycle, and wetland protection is an important way to achieve carbon neutrality and synergistic benefits between humans and ecosystems in China. However, most of the previous studies lack the influencing factors and driving paths of wetland carbon storage (WCS) from the perspective of coupled systems. Based on the SD-PLUS-InVEST coupling model and the socio-economic and ecological sustainability (SEES) coupling framework, this paper reveals the driving-feedback mechanism of wetland areas (WAs) from 2005 to 2060, and realizes the dynamic monitoring of WAs and a detailed assessment of WCS in Dongying city. The results show the following: WAs have a positive feedback relationship with the water resources and ecosystem service (ES) subsystems but a negative feedback relationship with the population and economic subsystems. The business as usual (BAU) scenario led to a malignant transformation of natural wetlands (NWs)-artificial wetlands (AWs)-production land (PL), increasing WCS by expanding AWs. The coordinated sustainable development (CSD) scenario created a virtuous PL-AW-NW transformation, increasing 421,700 tons of WCS compared with BAU. This study holds great significance for the protection, restoration, and sustainable development of wetland resources in coastal cities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Incentive initiatives on energy-efficient renovation of existing buildings in China were analyzed. • The dynamic evolution of incentive initiatives was analyzed from different perspectives. • A critical analysis of representative initiatives in two batches of pilot cities was conducted. • Implementation barriers and limitations on energy-efficient renovation of existing buildings were summarized. • Recommendations and pathways of promoting related incentive initiatives were proposed. Under China's national strategy of carbon neutrality by 2060, it is urgently necessary and challenging for the governments to proactively explore policy tools to facilitate energy-efficient renovation of existing buildings. Currently, a considerable number of studies have been conducted on building energy-efficient renovation and its derivative topics, however, a comprehensive overview on incentive initiatives related to existing renovation practices in China is still scarce, such as a lack of critical correlation analysis between national and local initiatives, a lack of the synthesis and critique towards the latest policies and related achievements, and inadequate generalization of the diverse and multi-layered barriers and challenges in building energy-efficient renovation practices. To address these issues, this paper adopts a diversified policy segmentation approach to deeply analyze the dynamic evolution of the incentive initiatives from both national and local level perspectives, as well as to establish the related network of policy linkages between national to local, and between different localities. In addition, this paper presents a critical analysis on representative initiatives in two batches of pilot cities, and proposes good practices and valuable experiences for building energy-efficient renovation. Finally, this paper further summarizes and discusses the barriers to building energy-efficient renovation from four perspectives: governments, householders, enterprises and research institutions, and proposes a series of targeted and feasible pathways and strategies. This study can provide theoretical guidance and targeted recommendations for the formulation of policies, standards and regulations for building energy-efficient renovation in China. [ABSTRACT FROM AUTHOR]

• Propose a methodology for measuring of regional synergistic emission reduction. • Regional synergistic emission reduction networks have a multilevel nested structure. • Explained the evolution characteristics of regional synergistic emission reduction. • Identified driving factors of regional synergistic emission reduction networks. Synergistic regional emission reduction (SRER) is an important part of the strategy to achieve carbon neutrality. In this paper, a system efficiency value-added model is proposed to measure the strength of SRER network relationships for 40,186 sets of city pairs and the SRER levels of 284 cities in China from 2011 to 2021. Subsequently, the evolution characteristics of the SRER network, the spatial and temporal characteristics of urban SRER levels, and the driving factors of the SRER network evolution are further analyzed. The results are as follows: SRER network is shifting from a fan-shaped network pattern of "three-region clustering and sporadic coverage" to a shuttle-shaped nested network pattern of "east-west linkage and multi-region support". Time series analysis found that urban SRER level in China showed fluctuating growth characteristics during the study period. Spatial analysis reveals that the spatial autocorrelation of urban SRER level is decreasing, and the spatial distribution range is expanding towards the southwest. Emission reduction comparative advantages (CAER), green innovation division of labor (GIDL) and urban economic linkages (LCEL) are driver factors of SRER network evolution. This study proposes an evaluation method for the urban SRER level from network relationships perspective. Subsequently, network evolution characterization, spatial and temporal characterization, and driving factor analysis were conducted. The results of the study provide important theoretical guidance and inspiration for grasping the current status and enhancement paths of China's urban SRER level. [ABSTRACT FROM AUTHOR]

With the establishment of carbon peaking and carbon neutrality goals in China, developing low-carbon cities has become crucial. Previous studies primarily focus on the quantitative structure of land use to achieve low carbon optimization, while ignoring the spatial integration between urban form and carbon emissions. To fulfill this gap, this paper proposed a dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model, utilizing idea-point multi-objective linear programming (IMLP) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the low-carbon-emission pattern of the city at both the structural and spatial scales respectively. By innovatively linking the urban shape index with carbon emissions, this model aims to minimize urban carbon emissions, while maximizing economic development, urban development suitability, and the compactness of city. The proposed method is applied to Wuhan's main urban area in China, the DOLA model shows significant improvements in target values across all scenarios. Among them, the scenario that prioritized low carbon emissions at both the structural and spatial scales achieved the most significant reduction of 6.43 % in carbon emissions. The DOLA effectively contributes to achieving multiple spatial targets, thereby aiding in reducing carbon emissions. It offers technical support for developing pathways towards China's "dual carbon" target. • A dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model is proposed. • The DOLA model innovatively incorporates urban form indices to carbon emissions. • The DOLA model achieves spatial aggregation targets, thereby facilitating significant reductions in carbon emissions. • The DOLA model generates a series of land use optimization scenarios, offering a valuable reference for land use planning. [ABSTRACT FROM AUTHOR]

Currently, data is increasingly becoming a crucial production factor and strategic resource for economic development. However, there is no consensus on whether data plays a significant role in promoting city green development. This paper examines the effect and the mechanism of big data on carbon emission reduction based on a quasi-natural experiment of Big Data Comprehensive Pilot Zones (BDCPZ) in China, using a difference-in-difference approach and combining panel data from 261 cities in China from 2010 to 2019. The study found that: (1) The policy of BDCPZ significantly reduced carbon emissions, which confirms the critical importance of the digital dividend in China's green development process. (2) We find the emission reduction effect of BDCPZ is achieved in three main ways: promoting industrial upgrading, improving energy use efficiency and increasing green TFP in manufacturing. (3) Further analysis reveals that the carbon emission reduction effect of BDCPZ varies depending on city population size, levels of manufacturing development, resource endowments, and pilot cities with low carbon related policies. This paper empirically confirms the digital dividend in city green transformation, provides evidence and policy guidance for the broad application of the digital economy in the field of green and low-carbon development, and enriches research in development economics and environmental economics. It helps to provide novel policy insights for the government to implement proactive policies of carbon reduction, and to build a diversified carbon reduction governance system to successfully achieve "carbon peak" and "carbon neutral" targets. • We empirically identified the digital dividend in China's green transformation using the BDCPZ policy. • We explored three mechanisms of BDCPZ construction for green development. • The digital dividend is greater in large cities, advanced manufacturing bases and non-resource-based cities. • The digital dividend is amplified in pilot cities for carbon trading and low-carbon cities. [ABSTRACT FROM AUTHOR]

• The spatiotemporal interactions of carbon emission efficiency(CEE) were explored. • A MGWR model was used to estimate the scale effects of factors influencing CEE. • The evolution of the spatial pattern of CEE had strong spatial integration. • CEE agglomeration changes had path dependence and spatial locking. • The factors influencing CEE exhibit obvious scale heterogeneity. Exploring the spatiotemporal evolution and influencing factors of carbon emission efficiency (CEE) is crucial for achieving the goal of urban carbon neutrality. However, most of the existing studies ignore the temporal dependence of the spatial pattern evolution of CEE and the scale variability of the factors influencing CEE. With the help of an exploratory spatiotemporal data analysis framework, this paper examined the spatiotemporal interactions of CEE across 110 cities in the Yangtze River Economic Belt (YREB). In addition, a multiscale geographically weighted regression model was employed to reveal the scale effects of influencing factors on CEE. The main conclusions are as follows: first, the CEE of cities in the YREB shows a fluctuating upward trend, but the overall level is still low, and a certain polarization phenomenon exists. Second, the spatial pattern of the CEE of cities in the YREB is generally relatively stable, with strong spatial integration and path dependence. Finally, the factors influencing CEE exhibit obvious scale variability and spatial heterogeneity. Our findings can provide a basis for localized and differentiated carbon emission reduction decision-making at the city level, as well as new insights for the formulation of sustainable urban spatial planning and low-carbon development strategies. [ABSTRACT FROM AUTHOR]

• This paper explores the effect of green finance on SDGs. • GFPZ significantly promotes SDGs. • Green finance indirectly affects SDGs through technological innovation. • The impact of green finance on SDGs exhibits heterogeneity. In pursuit of actively advancing energy transformation, ensuring the attainment of carbon peak and carbon neutrality objectives, and fostering sustainable development, China has dedicated substantial efforts. This article treats the Green Finance Reform and Innovation Pilot Zones (GFPZ) as a quasi-natural experiment, thereby constructing an analytical framework for evaluating the impact of green finance on achieving Sustainable Development Goals (SDGs). We empirically investigate this impact by analyzing data from 316 cities in China spanning the period from 2011 to 2020, employing a difference-in-differences (DID) model. The study suggests a gradual improvement in the level of sustainable development in China. Moreover, green finance contributes to SDGs, and the conclusion is robust. Further investigation reveals that green finance improves sustainable development by fostering technological innovation. Heterogeneity results indicate that the influence is notably more pronounced in western regions and locales characterized by stringent environmental regulations. This article offers fresh perspectives on achieving SDGs. [ABSTRACT FROM AUTHOR]

The aim of the paper is to evaluate empirically the main implications of digital technology (DT) on carbon emissions (CE). Our sample focuses on 283 cities of China over the period from 2003 to 2019, and the effect of DT on CE is examined with a threshold spatial effect model. Empirical results show that DT has a negative impact on carbon emissions and the transmission mechanism contains both a direct impact on local region and a spillover effect across regions. There is empirical evidence of a threshold effect in the relationship between DT and CE, with stronger carbon reduction effect for DT values larger than the established threshold. Moreover, CE reduction effect of DT appear to be amplified by the level of marketization. Regions with a higher level of marketization thus show a more significant manifestation of CE reduction effect attributed to DT. Our results provide empirical evidence that DT development may contribute to achieving carbon neutrality at least partially. [ABSTRACT FROM AUTHOR]

The comprehensive cohesion of the digital economy, structural deviation adjustment, and green development is a crucial step towards promoting high-quality economic growth and realizing carbon peak and carbon neutrality. This paper probes the impact of intra- and inter-regional digital economy on carbon emissions, considering the mediating effect of structural deviation, utilizing panel data from 267 prefecture-level cities in China spanning the years 2011–2019. The findings reveal the following key insights. First, the connection between the digital economy and carbon emissions within cities follows an inverted "U" shape. In the early stages, the digital economy leads to an average increase in carbon emissions of 0.069%. However, in the later stages, the digitized economy markedly decrease emissions. Second, both the digital economy and carbon emissions exhibit spatial spillover effects. Within the region, DE has a significant nonlinear spillover effect on CO 2 emissions in the neighboring regions, with the spatial decay boundary of this effect extending up to 380 km. Third, analysis of the impact mechanism reveals that the digital economy mitigates local and neighboring carbon dioxide emissions by adjusting structural deviation. Heterogeneity analysis indicates that the digital economy is more likely to have a carbon emission reduction effect in cities that are not involved in the industrial transfer and those that do not belong to the old industrial base. These findings provide a rationale and policy reference for China to harness the opportunities presented by digital economy development and to achieve the "dual-carbon" goal. [ABSTRACT FROM AUTHOR]

Balancing the ecological and economic benefits of energy consumption is an essential requirement and a key part of achieving the task of high-quality economic development and the goal of carbon neutrality. This paper analyses the energy efficiency and spatial and temporal evolution characteristics of thirty Chinese provinces and cities from 2006 to 2019 based on the DSBM model. The results show that, firstly, environmental factors have a significant impact on the evaluation of energy efficiency, with the national energy efficiency showing phase fluctuations and a decreasing trend from the east to the center and then to the west during the sample period. Secondly, the country and the three major regions did not show a σ convergence. Thirdly, there is significant absolute and conditional convergence in overall energy efficiency after considering spatial spillover effects, and with a turning point. Fourthly, energy efficiency in the three major regions also shows a clubbing phenomenon, with different regions tending to their own steady state of energy efficiency. Finally, we put forward suggestions for improving energy efficiency. The results of the study help to understand the changing trends and influencing factors of inter-regional energy efficiency in China, which can provide a reference for achieving the transformation of energy structure and has important practical significance. [Display omitted] • The DSBM model to evaluate energy efficiency relaxes the assumptions of DEA models. • The DSBM model includes environmental and relaxation variables in EE assessment. • The spatio-temporal characteristic of EE is analyzed from multifaceted perspectives. • We analyze the club characteristic and key turning points of EE convergence in China. [ABSTRACT FROM AUTHOR]

Existing literature has carried out fruitful research on the effects of environmental regulation policies. Due to differences in policies, regions, and data analysis methods, however, the previous studies have not reached a consensus on the effects of environmental regulation policies, especially on the effects of environmental regulation policies of developing countries from the perspective of environmental justice. This paper takes the establishment of the environmental court in China's intermediate people's court as a quasi-natural experiment and uses the multi-period difference-in-differences method to examine the effect of the establishment of the environmental court on environmental governance and economic performance, based on the panel data of 284 cities in China from 2003 to 2019. The results show that the establishment of the environmental court can significantly reduce the emission intensity of industrial SO 2 in the short term. This reduction effect is resulted from the joint emission reduction effect of the establishment of the environmental court on local cities and adjacent cities. The mechanism analysis shows that the establishment of the environmental court has the dual effect of emission reduction without hindering economic growth. This effect is mainly achieved by improving energy efficiency, stimulating green technology innovation, increasing environmental investment, and improving the green finance level. Furthermore, the emission reduction effect of the establishment of the environmental court shows differential effects under the constraints of regional rule-of-law level, environmental awareness, fiscal pressure, and level of innovation and entrepreneurship. In addition, the establishment of the environmental court can facilitate the achievement of carbon peaking and carbon neutrality goals. • Evaluated the impact of the improvement of judicial quality on environmental governance and economic performance. • The establishment of the environmental court would reduce the emission intensity of industrial SO 2. • The establishment of the environmental court would not compromise regional productivity. • There is heterogeneity in the policy effects of the establishment of the environmental court. • Effects are achieved via energy efficiency, green technology innovation, environmental investment, and green finance. [ABSTRACT FROM AUTHOR]

As an important path to realizing carbon neutrality in China, digital development may also lead to the growth and rebound of energy demand. Investigation of digital rebound effect is important to evaluate whether digital development can effectively conserve energy. Thus, this paper examines the impact of digital development on electricity consumption and its heterogeneity among Chinese cities, and further evaluates the sizes of energy rebound effect triggered by digital development. The results show that, first, electricity consumption in Chinese 285 cities increases by 7%–20% due to digital development, and this positive influence tends to increase with the increase in electricity consumption. Second, in the eastern, high-income, and high-tertiary industry share regions, digital development has a greater role in promoting electricity consumption. Third, the digital development of Chinese cities induces the rebound effect of electricity consumption, and even induces the backfire effect in the early stages of digital development. Finally, the energy rebound effect tended to decrease over time, with a partial rebound in electricity consumption, and the minimum rebound effect was 22%. The results highlight the necessary attention on digital rebound effect, and the importance of low-carbon digital development. • Electricity consumption increased 7%–20% due to digital development in China • The positive influence tends to increase with the increase in electricity consumption • Digital development in Chinese cities leads to energy rebound effect • The energy rebound effect tended to decrease over time, with a partial rebound [ABSTRACT FROM AUTHOR]