CO2 Emissions In China Research Articles

The impact of environmental regulation on green TFP：A quasi-natural experiment based on China's carbon emissions trading pilot policy

The carbon emissions trading pilot policy in China serves as a crucial market-oriented regulation tool to achieve green development. This study explores the impact of this policy on Chinese green TFP (GTFP). We utilize the provincial panel data from 2000 to 2017 and employ the GNDDF-Luenberger model to characterize GTFP. The results show that the carbon emissions trading pilot policy significantly improves GTFP. The effects are more pronounced in regions with more foreign direct investment, smaller technological gaps and higher levels of marketization. The carbon emissions trading pilot policy improves GTFP by promoting practical innovation. Furthermore, the promotion of GTFP through the carbon emissions trading pilot policy primarily relies on the enhancement of technological efficiency, rather than technological improvement. Our study provides policy implications for establishing an effective carbon emissions trading market in China.

The impact of opening the Arctic Northeast Passage on China's carbon emissions

The increase in shipping along Arctic routes has resulted in an increasing need for the environmental effects of shipping to be considered. Existing studies on the environmental impact of Arctic routes have focused mainly on shipping operations and have paid little attention to the dynamic features of opening new shipping routes. In light of the continuing retreat of Arctic sea ice, this study aimed to predict changes in China's CO2 emissions from a production perspective via scenarios of the opening of the Arctic Northeast Passage (NEP) in different future years (2025, 2030, 2035). A shipping cost model was first developed to calculate the changes in shipping costs resulting from commercial use of the NEP. Then, an energy-environmental version of the Global Trade Analysis Project (GTAP-E) model and the GTAP-E 10.0 database were used to simulate the changes in CO2 emissions that may arise from the economic adjustments to the opening of the NEP. The driving factors and possible mechanisms of the environmental effects of the NEP were analyzed based on the simulation results. The re-simulation for the environmental effects of opening the NEP taking emission control policies into account was performed. The findings showed that the opening of the NEP would increase global CO2 emissions. This effect would gradually increase over time. For China, opening the NEP would initially reduce short- and medium-term CO2 emissions owing to upgrades of sectoral structure, while ultimately elevating emissions in the long run due to a large expansion in trade and the economy. The economic growth driven by commercial use of the NEP would tend to optimize the Chinese sectoral and energy structure in the short run. Notably, emission control policies would markedly affect the aforementioned environmental outcomes of opening the NEP.

The research model of forecasting agricultural carbon emissions based on ARIMA-LSTM

To accurately forecast the volume of agricultural carbon emissions in China, this paper proposes a forecasting model that combines the Autoregressive Integrated Moving Average Model (ARIMA) with Long Short-Term Memory networks (LSTM) to better handle both linear and non-linear parts in time series. Using the data on China's agricultural carbon emissions collected from 2000 to 2023 as the research subject, the data from 2000 to 2016 were first used as the training set, and the data from 2017 to 2023 were used as the test set. The Spearman's rank correlation coefficient method was utilized to study the correlation between various types of carbon emissions. Then, the established ARIMA-LSTM forecasting model was applied to predict the emissions in the test samples and make comparisons. The Absolute Relative Error (ARE) was adopted as the evaluation metric for different models, comparing the prediction errors of the ARIMA-LSTM model with those of the ARIMA and LSTM single models through analysis. The results show that the positive correlation between diesel carbon emissions and film carbon emissions was the highest; the absolute relative error of the ARIMA-LSTM model did not exceed 7%, which is significantly lower than the other two comparison models. This research model achieves high-accuracy predictions of carbon emissions, which is beneficial for the control and management of the greenhouse effect.

Leveraging AI techniques for predicting spatial distribution and determinants of carbon emission in China's Yangtze River Delta

This study focuses on the prediction and management of carbon emissions (CE) under the backdrop of global warming, with a particular emphasis on developing spatial planning strategies for urban clusters. In this context, we integrate artificial intelligence technologies to devise an optimized spatial analysis method based on the attributes of multi-source, urban-level spatio-temporal big data on CE. This method enhances both the accuracy and interpretability of CE data processing. Our objectives are to accurately analyze the current status of CE, predict the future spatial distribution of urban CE in the Yangtze River Delta (YRD), and identify key driving factors. We aim to provide pragmatic recommendations for sustainable urban carbon management planning. The findings indicate that: (1) the algorithm designed by us demonstrates excellent fitting capabilities in the analysis of CE data in the YRD, achieving a fitting accuracy of 0.93; (2) it is predicted that from 2025 to 2030, areas with higher CE in the YRD will be primarily concentrated in the 'Provincial Capital Belt' and the 'Heavy Industry Belt'; (3) the economic foundation has been identified as the most significant factor influencing CE in the YRD; (4) projections suggest that CE in the YRD are likely to peak by 2030.

IS CHINA REALLY A “POLLUTION HAVEN”: HOW FOREIGN DIRECT INVESTMENT SHAPE CARBON EMISSION IN CHINA? EVIDENCE FROM 265 CHINESE PREFECTURE-LEVEL CITIES

This paper explores the applicability of the well-known “Pollution Haven” hypothesis and “Pollution Halo” hypothesis at the city-level in contemporary China. The fixed effect model and the threshold effect model are employed to investigate the relationship between foreign direct investment (FDI) and carbon emission in 265 Chinese prefecture-level cites. Based on the entire sample, the results of fixed effect model support the applicability of “Pollution Haven” hypothesis, while the hypothesis is substantiated primarily in eastern and western China. In contrast, the central part of China does not conform to either hypothesis, suggesting that the emergence of a distinct “Pollution Haven Basin” within the country. Unlike the inverted “U-shaped” curve commonly posited in the existing literature, the threshold effect model unveils a “U-shaped” connection between FDI and carbon emission. Specifically, we find that the turning point is at around $52.93 million, which implies that FDI needs to surpass a certain threshold to actively drive carbon emission in the presence of a “Pollution Haven Engine”. Accordingly, in the context of more in-depth international trade and China’s carbon neutralization commitment, it would be wise for the Chinese local governments to control the FDI scale, and encourage FDI to tilt toward the clean sectors.

How does urbanization impact China's carbon emissions: A regional heterogeneity perspective.

In the context of China's green development and "dual carbon" goal, urbanization, as a way to achieve Chinese modernization, has a particularly important effect on green and low-carbon economic development. Firstly, this paper empirically analyzed the influence of urbanization on per capita carbon emissions using Chinese city data and a panel fixed-effects model. Then, the impact mechanisms of urbanization on carbon emissions were examined from both the demand and supply sides. Finally, we analyzed the differences in the transmission mechanisms of urbanization affecting carbon emissions in the eastern, central, and western regions. The results show that (1) urbanization increases per capita carbon emissions. However, this effect shows inter-regional differences, with more significant promotion effects in the eastern and central regions; (2) on the demand side, the residents' consumption intensity can drive carbon emissions, while the rise of human capital agglomeration suppresses carbon emissions; on the supply side, industrial structure can drive carbon emissions, while the increase of green technological innovation suppresses carbon emissions; (3) the consumption effect and the industry effect play a major role in the eastern and central regions, while the intermediary effect is not obvious in the western region. This study can provide important insights for synergizing urbanization and achieving carbon reduction commitments.

Mountains block and seas move: The impact of geographical environment on the China's Carbon Emissions Trading Scheme in reducing urban PM2.5 concentrations

The dispersion of PM2.5-related pollutants in the atmosphere is intricately connected to the natural geographical environment. Nevertheless, previous evaluations of environmental policies regarding urban PM2.5 concentrations have rarely taken into account the influence of the natural geographical environment. Utilizing panel data and DID models for 266 prefecture-level cities in China from 2006 to 2019, this study thoroughly examines the impact of geographical environment on the China's Carbon Emissions Trading Scheme (ETS) in reducing urban PM2.5 concentrations. The results suggest that (1) Neglecting the impact of natural geographical environment, although the ETS effectively reduces the PM2.5 concentration in the pilot cities from an overall perspective, there are anomalies in the robustness test and the heterogeneity analysis, which are mainly reflected in the decrease in the significance of the PSM-DID results, and the positive haze-enhancement effect of the ETSs of Fujian, Beijing, Shanghai, etc. (2) Considering the influence of geographic environment, combined with the spatial correlation between China's topographic distribution and PM2.5 concentration, four control groups of Basin, Coastal, etc. are screened for group regression, and the results show that geographic environment factors significantly affect the emission reduction effect of each ETS on PM2.5 concentration. (3) The regression results of the three types of spatial econometric models all indicate that there is a significant spatial spillover effect of ETS to reduce PM2.5 concentration. Considering the influence of geographic environment on the spatial spillover effect and the fact that China's provincial division is mostly based on geographic environment factors, in order to more reasonably assess the effect of the policy on the PM2.5 concentration, the researcher should select sample cities with no spatial correlation of PM2.5 and clustering robust standard errors to the provincial level.

Quantitative Carbon Emission Prediction Model to Limit Embodied Carbon from Major Building Materials in Multi-Story Buildings

As the largest contributor of carbon emissions in China, the building sector currently relies mostly on enterprises’ own efforts to report carbon emissions, which usually results in challenges related to information transparency and workload for regulatory bodies, who play an otherwise vital role in controlling the building sector’s carbon footprint. In this study, we established a novel regulatory model known as QCEPM (Quantitative Carbon Emission Prediction Model) by conducting multiple linear regression analysis using the quantities of concrete, rebar, and masonry structures as independent variables and the embodied carbon emissions of a building as the dependent variable. We processed the data in the detailed quantity list of 20 multi-story frame structure buildings and fed them to the QCEPM for the solution. Comparison of the QCEPM-calculated results against the time-consuming and error-prone manual calculation results suggested a mean absolute percentage error (MAPE) of 2.36%. Using this simplified model, regulatory bodies can efficiently supervise the embodied carbon emissions in multi-story frame structures by setting up a carbon quota for a project in its approval stage, allowing the construction enterprise to carry out dynamic control over the three most important audited building materials throughout a project’s planning and implementation phase.

Study on the Impact of China's Digital Economy on Agricultural Carbon Emissions

<p _msthash="770" _msttexthash="18511649104"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Under the background of China's “double carbon” goal, digital economy has become an important way to reduce carbon emissions in China. This paper utilizes the provincial panel data of China from 2012 to 2022, introduces the perspective of agricultural science and technology innovation, empirically examines the impact mechanism of regional digital economy development on agricultural carbon emission through regression analysis model, and portrays the dynamic effect and spillover effect of digital economy development on agricultural carbon emission from both time and space dimensions. The empirical results show that: digital economic development will have a significant inhibitory effect on the intensity of agricultural carbon emissions, and the inhibitory effect will be indirectly affected through the path of agricultural scientific and technological innovation; the impact of digital economic development on the intensity of agricultural carbon emissions there is a time lag effect, the current stage of the digital economic development will still have a strong inhibitory effect on the intensity of agricultural carbon emissions in the future; Digital economic development has a spatial spillover effect, i.e., the development of the regional digital economy will have an inhibitory effect on the intensity of agricultural carbon emissions in neighboring provinces. Based on this, it is proposed to strengthen the construction of digital infrastructure, promote the coordinated development of the digital economy in the region, and formulate policies to reduce carbon emissions in agriculture.</span></span></p>

Digital economy's role in shaping carbon emissions in the construction field: Insights from Chinese cities

Achieving net-zero greenhouse gas emissions in the construction sector is essential to sustainable development policy. Previous studies have ignored the impact of the digital economy on carbon emissions in the construction industry. Using relevant city-level data from China from 2011 to 2021 and employing ArcGIS and Stata software, this study examines the current status of carbon emissions from the construction industry and investigates the impact of the digital economy on these emissions. The findings reveal that (1) from 2011 to 2021, carbon emissions of the construction industry have a pattern of decreasing-increasing-decreasing-increasing across the country. There is an increasing concentration of areas with high and relatively high carbon discharges in Heilongjiang, Jilin, Ningxia, Inner Mongolia, Shanxi, Guangxi, and Guizhou. (2) The standard deviation ellipse indicates the core area of carbon emissions in China's construction industry is moving northeast and becoming more decentralized. (3) Through a series of robustness tests, the digital economy has been proven to effectively reduce carbon emissions from the construction sector in municipal areas. (4) In severely cold regions, mild regions, and high-population-density cities, the digital economy reduces building sector's carbon output. However, it stimulates carbon emissions in the hot summer and cold winter regions. (5) Mechanism tests show that the digital economy reduces China's urban construction carbon emissions by fostering technological progress and green innovation. Urban resilience further strengthens the contribution of the digital economy to reducing carbon discharges in the urban construction sector of China. This article presents empirical evidence demonstrating how the digital economy empowers the construction industry to curtail emissions.

Comprehensive analysis of carbon emission reduction technologies (CRTs) in China's coal-fired power sector: A bottom-up approach

Carbon dioxide (CO2) reduction technologies (CRTs) in the coal-fired power sector play an imperative role in the mitigation of environmental challenges and reducing CO2 emissions to help achieve the 2 °C target. However, a compelling necessity persists for a unified framework that can effectively and accurately estimate the costs and potentials associated with CRTs, arising from the diversity of technologies and unit types. Therefore, this study employs a bottom-up approach to analyze the costs and potential of 25 advanced CRTs in the coal-fired power sector, excluding CO2 capture, utilization and storage (CCUS), across 19 types of coal-fired power units. This analysis amalgamates the CO2 reduction supply curve (CRSC) approach with levelized cost of CO2 reduction (LCOC) and a broken-even analysis which could reflect the sector's average carbon reduction cost. The outcomes reveal the following key insights: (1) These technologies collectively harbor a substantial CO2 conservation potential amounting to 925.61 Mt CO2, with a broken-even price of CNY 410.1/tCO2. This reduction could potentially lower 20%–25 % of CO2 emissions in China's power system in 2022, highlighting the crucial role of CRTs in achieving a low-carbon transition and national climate targets; (2)Steam turbine flow path retrofit (T8) not only offers a relatively high CO2 reduction potential (>60 Mt CO2), but also features a low cost (<CNY 150/tCO2) and high profit (>CNY 0.85/MWh). Prioritizing the development of this technology can significantly accelerate the low-carbon transition of coal power; (3) Carbon price have a paramount influence on the cost-effectiveness of CRTs and driving their adoption.

How does green finance impact carbon emissions in China: Evidence from the fintech perspective.

In light of China's objectives for carbon peak and carbon neutrality, there is an opportunity for fintech to leverage its technological advantages and enhance its integration with green finance (GF). This can bring about enhanced coverage and precision of financial services for green industries, facilitating the transformation towards a sustainable, greener, and low-carbon real economy. We investigate how fintech development influences the carbon emission reduction effects of GF utilizing a two-way fixed effects model with a panel dataset covering 30 provinces in China from 2011 to 2020. Our findings indicate that the negative impact of GF on carbon emissions (CE) is heightened in areas with high levels of fintech development. Furthermore, we notice regional disparities in how fintech development impacts the effectiveness of GF in reducing CE. Specifically, fintech has a statistically significant impact in the central and western regions, whereas its significance is absent in the eastern region. Lastly, our mechanism analysis reveals that fintech plays a vital role in enhancing GF's capacity to mitigate CE, which is achieved through channels of promoting green technology innovation (GTI), alleviating corporate financing constraints (FC), and optimizing energy structure (ES). These findings provide compelling evidence for the positive effect of fintech on the environment and offer justification for promoting the development of fintech and GF.

The roots of carbon emission differences between China and USA: A quantitative study based on global value chains

China and the USA, as preeminent contributors to global carbon emissions, demonstrate discernible differentials in both magnitude and trajectories of their respective carbon outputs. This article employed two methods, Structural Decomposition Analysis (SDA) and Quantitative Structural Modeling, to scrutinize the underpinnings of these disparities through the lens of the global value chain. Drawing upon data from the World Input-Output Database (WIOD), our analysis revealed that the compounded influences of output composition, input intensity, input composition and input origin collectively elevated China's aggregate carbon footprint from 2000 to 2014, while the scale effect made China's carbon emissions lower than of the USA. Notably, China's carbon emissions surpassed those of USA, with the gap accentuating over time. The quantitative results of the structural model showed that the difference in carbon emissions between China and USA predominantly stem from disparities in productivity, production technology, factor intensity, factor endowment and direct carbon intensity. Differences in trade costs exhibited some discernible impact, their influence remains relatively marginal, whereas distinctions in consumption behaviors and trade imbalances minimally contribute to the observed differentials. These findings have important policy implications for global carbon reduction efforts and China's trajectory towards a low-carbon economic paradigm.

Analysis of Carbon Emission Reduction with Using Low-Carbon Demand Response: Case Study of North China Power Grid

The power sector is the single industry with the largest carbon emission in China, the carbon emission of which accounts for more than 40% of China’s total carbon emissions. In relevant research on the simulation of power system operation, current studies focus more on energy conservation and economical operation, while few consider the low-carbon optimization of the power system from the perspective of carbon emissions. In addition, in relevant research on carbon reduction in the power system, current studies focus more on controlling the direct carbon emission of the source side and less on the indirect carbon emissions of the load side, which focus on the reverse effect of a user’s electricity consumption behavior on the carbon reduction goals of the power system. This article delved into a deterministic simulation model of power system operation based on time series load curves and proposed a carbon reduction mechanism called the low-carbon demand response mechanism, which guides users to actively respond and reduce the carbon emission of power systems. In addition, this article conducted an empirical analysis based on the planning data of the North China Power Grid. To minimize carbon emissions, a simulation of low-carbon optimization operation for the North China Power Grid in 2040 was carried out. Then, based on the simulation results, an analysis of the carbon reduction benefits of low-carbon demand response was carried out. Ultimately, the empirical analysis verified that low-carbon optimization operation and low-carbon demand response technology possess significant carbon reduction potential for the power system.

Spatial-temporal characteristics and driving factors of carbon emissions from the construction industry in the Belt and Road region of China

To promote sustainable economic development in the Belt and Road region of China, reducing carbon emissions is essential. The construction industry is a major contributor to carbon emissions in China. Therefore, studying the dynamic evolution of carbon emissions from the construction industry in this region and its driving factors is of great significance for effectively controlling emissions and achieving China’s carbon peak and carbon neutrality targets. This paper first employs the Slope model, Moran’s I index, and standard deviation ellipse to reveal the spatial-temporal characteristics of carbon emissions from the construction industry, and then applies the geographical detector model to identify the main driving factors of carbon emissions. The results indicate that: (1) From 2006 to 2021, the total carbon emissions showed a fluctuating growth trend, and there were significant differences in emissions among different regions. (2) Carbon emissions in most provinces exhibited a moderate growth trend, and there was significant spatial correlation and aggregation of inter-provincial emissions. Regional carbon emissions from 2006 to 2021 showed a spatial distribution pattern from northeast to southwest, with a weakening trend, and the center of gravity mainly distributed in the east of the region. (3) Labor input, urbanization rate, total output value of the construction industry, degree of opening up, and energy intensity are the main factors influencing the spatial heterogeneity of carbon emissions from the construction industry, and the majority of the interaction types between factors were bivariate enhancement. This study aims to provide theoretical support for policymakers to formulate appropriate policies for building energy conservation and emission reduction.

The Impact of Digitalization on Carbon Emissions in China: A Spatial Analytical Perspective

The Impact of Digitalization on Carbon Emissions in China: A Spatial Analytical Perspective

Impact of China's carbon emissions trading scheme on firm-level pollution abatement and employment: Evidence from a national panel dataset

As an emerging economy, China has implemented market-oriented carbon emissions trading schemes in response to growing environmental challenges. Yet, concerns persist regarding the potential hindrance of these policies on socio-economic progress due to increased costs and possible declines in employment rates. Using a novel rich panel dataset of small-medium enterprises from 2011 to 2015, this study employs time-varying difference-in-differences methods to provide robust empirical evidence on the effect of these carbon trading policies. The findings reveal that the trial programs notably decreased sulfur dioxide emissions at the firm level by 2–3%, concurrently fostering a 6–7% increase in employment, indicating the realization of a ‘double dividend.’ An analysis of variations demonstrates that non-state enterprises, foreign-owned companies, and industries with substantial pollution levels experienced the most significant benefits, particularly with heightened positive impacts on employment observed in Western regions. Upon delving into the mechanisms involved, it becomes evident that initial job losses were offset by the emergence of new positions due to the expansion in output and the effects of substituting factors resulting from green investments catalyzed through the carbon trading initiative. These compelling findings strongly advocate for the widespread implementation of market-based tools on a national scale, providing robust support for concurrently addressing environmental concerns and promoting livelihood objectives within the specific context of China.

Nexus among government digital development, resource dependence, and carbon emissions in China

Government digital development uses data technology to empower data for economic and social benefits, making social governance more scientific and efficient. Using unbalanced panel data of Chinese cities from 2006 to 2019, this paper explores the relationship between government digital development, fossil energy dependence, and urban carbon emissions. The results show that government digital development can effectively curb urban carbon emissions. The " carbon curse " phenomenon does exist at the city level in China, and government digital development is an effective way to break the "carbon curse." Heterogeneity analyses show that the digitalization of government significantly reduces the carbon emissions of cities in the East-Central region and the Yangtze River Economic Belt. Promoting government digital development in large cities can effectively break the "carbon curse." In addition, government digital development can also curb urban carbon emissions through structural, innovation, and resource allocation effects. Therefore, due attention should be paid to the digital development of government, which in turn will leverage digital advantages and eliminate the adverse effects of fossil energy dependence. This is of profound theoretical and practical significance for curbing urban carbon emissions and promoting green economic development.

Carbon Emission Heterogeneity in Developed Countries: Insights for China’s Neutrality Strategy

This study investigates the heterogeneity and driving factors of carbon emission reduction in developed countries, providing insights for formulating carbon neutrality strategies and plans in China. Firstly, typical developed countries worldwide are categorized into four modes: high energy consumption–high emissions, high energy consumption–low emissions, low energy consumption–low emissions and low energy consumption–high emissions, based on the “per capita energy consumption–emission intensity” classification method. Secondly, the LMDI decomposition model is employed to calculate the carbon emission-reduction driving effects of each mode, thereby analyzing the evolution path of carbon emissions. Finally, carbon emission-reduction policy recommendations are proposed based on the current situation of provincial carbon emissions in China. The research findings are as follows: (1) Carbon emissions in developed countries follow the Environmental Kuznets Curve (EKC) law, exhibiting an inverted U-shaped trajectory. In the per capita GDP range of USD 12,000–20,000, carbon emissions in most countries have reached or are close to their peak, with per capita carbon emissions in different modes decreasing and tending towards a level of 4000 kg/person. (2) There is heterogeneity in the EKC of carbon emissions in developed countries, which can be divided into technological, structural, comprehensive and clean types based on differences in emission-reduction factors. The trajectories of carbon emissions in most developed countries follow a clean path, while those in newly developed countries are constrained by environmental pressures and are expected to be between mixed and structural types before the emission peak, and tend towards a clean type after the peak. (3) Energy clean-up is the most direct and efficient method for carbon emission reduction, which can significantly and efficiently achieve carbon emission reduction. Industrial restructuring and technological progress play a significant role in suppressing carbon emissions. Policy recommendations are proposed as follows: first, reduce the proportion of fossil energy and increase energy cleanliness; second, promote industrial restructuring to reduce energy dependence; third, enhance production technology and improve energy-utilization efficiency; fourth, increase technological breakthroughs to support low-carbon transformation; fifth, correctly recognize the differences among provinces in China and formulate differentiated carbon emission-reduction policies.

Environmental Regulation, Industrial Structure and Low-Carbon Development

&lt;p&gt;In this paper, the provincial panel data of china from the year 2006 to 2019 are used to analyze the association among environmental regulations, industrial structure od industries and low-carbon development. The results show that regional industrial CO2 emissions in China's east, center, and west were greatly decreased by environmental restrictions. The most significant deterrent to carbon emissions in the central region was, notably, environmental regulatory measures. By encouraging improvements in industrial structure and increasing its efficiency, the study shows that these rules helped to lower industrial carbon emissions. The examination of the sub-sample mechanism reveals significant regional variations: an advanced industrial structure was a major force behind low-carbon development in the western region, whereas high-efficiency industrial structures were critical in the eastern region. Through structural changes in the business, environmental restrictions can effectively promote low-carbon economic growth, as this study highlights. Across a range of control factors, environmental regulation considerably decreased regional industrial carbon emissions by about 0.196 to 0.033. Trade openness, urbanization, and the development of human capital all helped to reduce carbon emissions by roughly 1.057 to 4.104. The efficacy of regulatory tools was verified by endogeneity tests, which yielded coefficients varying between -0.891 and -0.156 for distinct factors. The results offering actual proof of the varying effects of environmental laws in various geographical areas and highlight the necessity of specialized policy strategies to promote sustainable development.&lt;/p&gt;