Carbon Emission Intensity Research Articles

The Impact of High-Standard Scenic Areas Construction on County-Level Carbon Emissions and Its Spatial Spillover Effects: Evidence from a Quasi-Natural Experiment

This study investigated for the first time the tourism–carbon emissions nexus based on the destination construction perspective, using the China’s national scenic areas (CNSA) construction as a vehicle for concretization. A multi-source county panel dataset of 29,628 samples was constructed. The staggered Difference-in-Differences (DID) model and spatial DID model were further formulated. The findings show that: (1) the CNSA resulted in a 0.1024% reduction in carbon emission intensity (CEI) in treatment counties relative to non-treatment counties, and although the effect exhibits a delay, it persists and intensifies over time; (2) our heterogeneity results indicate that the inhibiting effect is significantly more pronounced in the western, eastern, and county subsamples; and (3) the spatial DID analysis reveals that the CNSA exerts a negative spatial spillover effect on CEI. This work enhances comprehension of the tourism–carbon emissions nexus, with implications for advancing regional carbon emission reduction policy strategies.

Quantification of Carbon Emissions of Building Decoration Processes

The continuous growth in building decoration activities has led to significant energy and material consumption, increasing carbon emissions in the construction sector. Existing literature frequently overlooks the carbon impact of building decorations. This study employs the life cycle assessment (LCA) method to quantify the carbon emissions associated with building decorations across five typic building types: residential, hospital, educational, sports cultural, and office buildings. Data were gathered using a mix of field investigations, document reviews, and semi-structured interviews, ensuring comprehensive coverage of all life cycle stages. The results reveal that carbon emission intensities of the studied building decorations ranged from 70.01 to 298.79 kg CO2 eq/m2, with the lowest emissions found in educational buildings and the highest in sports and cultural buildings. The decoration material production stage consistently emerges as the major contributor to emissions, accounting for over 50% of the life cycle of carbon emissions across all building types. The transportation stage also represents a significant share, contributing 18.6% to 24.5% across the building types. It also indicates that ceiling engineering as well as wall and column engineering are the primary carbon emission sources in terms of decoration activities. This study systematically compares the carbon emission characteristics of building decorations across multiple building types, addressing a gap that has been largely overlooked in the existing literature. It highlights the key sources of carbon emissions and proposes targeted mitigation strategies. The findings also suggest future research directions, including the application of innovative low-carbon materials, advanced construction technologies, and optimization of logistics. These insights lay a solid foundation for future low-carbon design and construction practices within the building sector.

Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case.

Analysis of Decoupling Effects and Influence Factors in Transportation: Evidence from Guangdong Province, China

In recent years, global environmental issues have become increasingly prominent. The transportation industry, as the fundamental sector of national economic development, is also characterized by high energy consumption and carbon emissions. Therefore, it is imperative to conduct research on the carbon emission problem within this industry. In light of the Tapio decoupling model, an analysis of the correlation between traffic carbon emissions and economic development in Guangdong province during 1999–2019 was carried out. With the aim of encouraging Guangdong province’s low-carbon transportation development, the factors affecting the transportation industry are analyzed utilizing the generalized Divisia index model (GDIM). We also introduced passenger and freight turnover as an influencing factor for analysis. The findings indicate that (1) Guangdong province’s traffic carbon emissions increased from 1999 to 2019; (2) the traffic carbon emissions’ decoupling effect is mainly “weakly decoupled”, and the overall decoupling effect is not strong in Guangdong province; (3) among the traffic carbon emissions’ factors, the effects of the production value of traffic and the turnover volume are at the forefront, and the effect of turnover volume has gradually exceeded the production value of traffic in recent years. The suppression of the intensity of carbon emissions is relatively large, while the suppression of the intensity of energy consumption and transport is relatively weak. Based on this, strategies were proposed to promote a cleaner energy mix, improve energy use efficiency, create energy savings, develop green technologies, and foster the restructuring of transportation.

A Review of Policy Progress in China's Ecological Civilization Demonstration Zone

Under the background of increasing urbanization rate and global warming, China urgently needs to address the issue of promoting economic growth while reducing the intensity of carbon emissions to achieve harmony between economic growth and environmental protection and promote sustainable development. This study takes China's ecological civilization demonstration areas construction policy as the main object of analysis. The literature review method would summarize the specific pathways of this policy to achieve carbon emission reduction in terms of eco-economy and eco-institution. By classifying and analyzing its existing carbon reduction pathways, this study found that reforms and innovations were made in this policy, mainly in eco-economy and eco-institution. China's Ecological Civilization Demonstration Zones (ECDZs) policy, to achieve carbon emission reduction and carbon neutrality, has used both green technological innovation and economic reforms to upgrade the industrial structure and improve the energy structure. However, the above policies are broad and cannot meet the regional differences. Therefore, this paper gives the corresponding recommendations: to increase the support of financial and financing policies, optimize the energy infrastructure in urban areas, and develop green transport and green buildings.

Digital finance, capital-biased and labor-biased technical progress: Important grips for mitigating carbon emission inequality

As an innovative financial model in the era of the digital economy, digital finance, with its inclusive characteristics, provides effective financial support for the realization of the carbon peak and carbon neutrality. To realize the “double carbon” goal, it is necessary not only to control the total amount and intensity of carbon emissions but also to consider the coordination and fairness of carbon emission reduction between regions. The existing literature has discussed the carbon reduction effect of digital finance, but few studies have focused on the impact and mechanism of digital finance on regional carbon emission inequality. On the basis of data from 287 prefecture-level cities in China from 2011 to 2021, this paper empirically examines the effect of digital finance on carbon emission inequality via a fixed effects model and convergence model. Digital finance not only reduces the Theil index of carbon emissions but also promotes the convergence of carbon emissions, which mitigates regional carbon emission inequality. The mechanism test shows that digital finance reduces the level of regional carbon emission inequality by promoting capital- and labor-biased technical progress in high-carbon emission regions. Heterogeneity analysis proves that digital finance can better exert its inhibitory effect on carbon emission inequality in regions with low financial agglomeration levels, weak industrial bases and small digital business scales. Further analysis indicates that digital finance can effectively reduce carbon emission inequality between regions, but the effect on carbon emission inequality within regions is not significant. The above findings provide a realistic reference for the formulation of differentiated digital finance policies to support carbon emission reduction.

Global techno-economic analysis of MBR for hospital wastewater treatment

This study comprehensively examines and characterizes global membrane bioreactor (MBR) practices for hospital wastewater treatment, focusing on development trends, technical performance (pollutant removal and carbon emissions), and economic costs, including both capital expenditures (CAPEX) for civil engineering and equipment procurement, and operational expenditures (OPEX) for electricity, membrane replacement, labor, and chemical costs, as well as system footprint. The results show that MBR has been widely used for hospital wastewater treatment for over two decades, with global applications and scales significantly increasing, especially after the COVID-19 pandemic. A notable shift in membrane types has occurred, with hollow fiber membranes dominating before 2010 and flat inorganic membranes gaining prominence after 2020. MBR not only effectively removes conventional pollutants but also greatly reduces pathogens, ARBs and ARGs before disinfection, thus alleviating the subsequent disinfection burden. In addition, MBR is a crucial step in the process of completely removing emerging contaminants (ECs) that pose significant environmental and health risks. The CAPEX of MBR has decreased at the technical level in recent years. MBR requires only 62 % and 21 % of footprint for conventional activated sludge (CAS) and biofilm-based processes, respectively. MBR's land-saving advantage offsets the CAPEX gap with CAS in high land-cost areas. From before 2010 to after 2020, membrane costs saw the largest reduction in OPEX, dropping by 71 %, while electricity consumption saw a 10.71 % reduction, now comparable to biofilm-based processes. Currently, MBR's OPEX (0.158 USD/m3) is only slightly higher than that of biological contact oxidation (0.138 USD/m3). MBR also minimizes sludge production, reducing both treatment costs and associated disposal risks. MBR exhibit minimal concerns of excessive carbon emissions, with a carbon emission intensity (1.11 kg CO2eq·m−3), only slightly higher than biofilm-based processes (0.92 kg CO2eq·m−3). This study demonstrates that MBR is the valuable and practical solution for hospital wastewater treatment, as well as a preferred choice for upgrading existing facilities.

Comparative Anslysis of Carbon Emission Reduction Policies in China’s Manufacturing and Transportation Sectors

This study evaluates China’s environmental policies, specifically the increase of carbon emission tax rates and the reduction of carbon emission intensity, by developing a New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE) model that incorporates energy consumption and carbon emissions. The production sector is segmented into manufacturing, non-manufacturing, and transportation, with transportation services acting as inputs for both manufacturing and non-manufacturing firms. We construct four cases within two scenarios, each characterized by distinct targets and policies. After calibrating and estimating the relevant parameters, we compare carbon reduction outcomes and economic fluctuations across the two scenarios. Our findings indicate that when targeting the same carbon emission tax rate increase or carbon emission intensity ratio reduction, policies that increase the carbon emission tax rate or reduce carbon emission intensity implemented in the manufacturing sector achieve greater carbon emission reductions compared to the same polices implemented in the transportation sector. Meanwhile, compared to the latter case, output experienced a greater decline, and inflation exhibited a more substantial increase in the former case. However, when the quantities of carbon emission reductions are approximately the same in the first quarter, policies that increase the carbon emission tax rate or reduce carbon emission intensity enacted in the transportation sector demonstrate superior performance relative to the same polices implemented in the manufacturing sector. This leads to smaller final output reductions and milder inflation increases. In summary, for equivalent levels of carbon emission reductions, policies implemented in the transportation sector yield more favorable economic outcomes than those applied in the manufacturing sector.

How does green technology innovation affect urban carbon emissions? Evidence from Chinese cities

Green technology innovation is regarded as a key catalyst for sustainable urban development. This study mainly utilizes 278 Chinese cities data from the China Urban Statistical Yearbook, adopting ordinary least squares (OLS) and mediation effect model to investigate the dual impact and mechanism of green technology innovation on urban carbon emissions. Besides, spatial Durbin model (SDM) and threshold model are employed to examine spillover and non-linear impact. The results indicate that: (1) Green technology innovation can significantly mitigate carbon emission intensity, but cannot reduce carbon emissions. Thus, both carbon emission reduction effect and rebound effect coexist, and there is heterogeneity across different cities. (2) Green technology innovation has the potential to decrease carbon intensity through optimizing energy intensity, government participation and basic life cleanliness. (3) There is a spatial spillover effect concerning the emission reduction impact of green technology innovation. (4) The relationship between carbon emission intensity and green technology innovation exhibits a non-linear impact under varying levels of energy intensity, government participation, industrial structure rationalization, and environmental regulation. Building upon these findings, policy recommendations outlined in this study encompass the reinforcement of technological utilization, avoidance of excessive governmental intervention, and adoption of strategies for regional connectivity and differentiation.

Exploring the role of trade credit in facilitating low-carbon development: Insights from Chinese enterprises

As the world's largest emitter of carbon, China is proactively addressing climate change in a responsible manner. The country has set an ambitious goal of achieving the most significant reduction in carbon emission intensity globally within approximately 30 years. In light of the transition to a new low-carbon environment, it is imperative that Chinese enterprises assume a more proactive role in embracing carbon neutrality as a social responsibility. The question of how to supplement the green transformation funds that have not yet been covered by green finance has become a significant issue for Chinese enterprises seeking to achieve sustainable development and promote high-quality development. This study employs hand-collected data on companies' carbon emissions from 2018 to 2023, using the fixed effects model to conduct a comprehensive investigation of the role of trade credit embedded within the supply chain in driving the green evolution of Chinese companies and reducing their carbon dioxide emissions. The findings demonstrate that trade credit has a significant impact on reducing CO2 emissions for Chinese businesses while also enhancing their commitment to adopting greener practices, their capacity for investing in green transformation, and the quality of their green outputs. The results of the heterogeneity analysis indicate that the acquisition of trade credit exerts a more pronounced suppressive influence on carbon emissions for heavy-polluting enterprises, non-state-owned enterprises, low-supplier concentration enterprises, and maturity stage enterprises. Similarly, the supply of trade credit notably curtails the carbon emissions for non-heavy polluting enterprises, non-state-owned enterprises, low-medium customer concentration enterprises, and maturity stage enterprises. The empirical findings of this study contribute to reconciling the substantial dilemma between corporate goals for energy conservation, emissions reduction, and production growth. This research offers key insights to support businesses in achieving a gentle transition towards energy efficiency and reduced carbon footprint in line with the overarching trend of sustainable and low-carbon development.

Influence of Digital Economy on Ecological Carbon Environment in North China Under Spatiotemporal Synergistic Evolution

North China is one of the significant geographic subregions in China, and it is quite important to analyze its digital economy and ecological carbon environment. Firstly, the digital economy level and ecological carbon environment in North China between 2010 and 2019 were measured. Then, the coupling coordination degree model (CCDM) was utilized to examine the impact of the digital economy on the ecological carbon environment. The digital economy level in North China consistently increased between 2010 and 2019. The average value rose from 0.163 in 2010 to 0.645 in 2019. Moreover, carbon emission intensity showed a year-on-year decrease, with an average reduction of 23.47% during the same period. The degree of coupling coordination displayed an upward trend, and Beijing showed the highest coordination among all provinces and cities. These results highlight the impact of the digital economy level on the ecological carbon environment. Some suggestions were proposed for the future progress of the digital economy and carbon emission in North China.

Exploring the Influence of Digital Economy Growth on Carbon Emission Intensity Through the Lens of Energy Consumption

Exploring the impact of new economic forms such as the digital economy (DE) on carbon emissions is crucial for China’s “dual carbon” goals. This paper assesses the impact of the DE on carbon emission intensity (CI) from a 2011–2021 perspective on energy consumption in 30 provinces (Hong Kong, Macao, Taiwan, and Tibet are excluded) by using a double fixed-effects model for evaluating the DE. Based on the results from 2011 to 2021, (1) China’s DE and CI differ spatially and temporally. In contrast to CI, DE shows a pattern of low in the northwest and high in the southeast. The changes are similar to Hu Huanyong’s line, with considerable changes in the southeast, especially the coastal region. (2) Chinese DE’s carbon control effects (−0.027) vary by region. A significant negative effect is seen in both the eastern and western regions, with the western region having the greatest negative effect (−0.030), and a positive but insignificant impact in the central region. (3) Total energy consumption (TEC), structure (EC) and efficiency (EI) are all pathways of its influence. Path changes in China are mainly dominated by dual paths (Accounting for over 47%), with fewer single and multiple paths. Among them, the main dual path types are TEC and EC in the east and central regions, TEC and EC, and EC and EI in the western regions; meanwhile, the paths are unchanged in most of the provinces in China, and the changed provinces are mainly in the west. Based on these findings, DE development policies based on regional energy consumption differentiation are crucial to reducing carbon emissions.

The effect of the digital economy on carbon emissions in China's construction industry: evidence from spatial econometric analysis.

Carbon emission reduction in the construction industry is vital for realizing sustainable development, and the development of the digital economy plays an important role in this process. The impact of the digital economy on reducing carbon emissions in the construction industry is empirically explored through econometric analyses on a sample of panel data from 30 provinces in China from 2012 to 2021. The empirical results show that developing a digital economy can significantly reduce the construction industry's carbon emission intensity. Additionally, this impact has a significant spatial spillover effect and can benefit the neighboring regions. The mechanism test shows that the digital economy can reduce carbon emissions by improving the technological level of the construction industry. Moreover, the inhibiting effects of the digital economy on carbon emissions in the construction industry vary across different regions. They are more pronounced in the eastern and western regions of low coal-consuming regions. These findings offer valuable insights for policymakers to help drive the deeper integration of the digital economy with the construction industry and facilitate its transition to low-carbon development.

Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.

The Promoting Effect of Green Bonds on Reducing Carbon Emission Intensity Through Energy Structure Transition

Climate change poses a significant threat to the sustainable development of all countries. The transition to low-carbon energy sources is a crucial strategy for reducing carbon emissions and mitigating climate change. We investigate the mediating role of clean energy consumption (EC) and fossil energy supply (ES) on the promoting of carbon emission intensity per unit of GDP (CO2/GDP) reduction by green bonds (GBs). We develop a mediating model to analyze how GB influences CO2/GDP reduction through EC and ES, utilizing panel data from 13 prefecture-level cities in Jiangsu province spanning the years 2007 to 2021. Additionally, we assess the model’s reliability through endogeneity and robustness tests. We find that GBs contribute to reducing CO2/GDP by facilitating the structural transition of energy supply and consumption. Furthermore, the development of GBs enhance the consumption of clean energy and plays a direct role in advancing the transition in structure of both energy supply and energy consumption. Notably, we observe heterogeneity in the effectiveness of GBs on CO2/GDP reduction across different regions. Therefore, it is imperative for the government to actively promote the development of GBs to achieve sustainable economic growth. Furthermore, both financial and energy policies should be tailored to align with the specific energy structures of various regions.

ESG performance and corporate carbon emission intensity: based on panel data analysis of A-share listed companies

This study investigates the impact of ESG performance on enterprise carbon emission intensity, using panel data from A-share listed companies over 2011–2022. The findings suggest that ESG can encourage enterprises to actively engage in environmental governance, enhancing their profitability and reducing carbon emission intensity, thereby achieving dual optimization of environmental and economic benefits. The mechanism test reveals the intermediary roles of institutional investors’ participation, total factor productivity, and green technology innovation. Heterogeneity analysis indicates that the relationship between ESG performance and enterprise carbon emission intensity varies with different degrees of management shortsightedness, ownership separation, equity balance, legitimacy status, and industrial pollution characteristics, reflecting the heterogeneous influence driven by Intrinsic motivation and external factors. Notably, the mitigating influence of ESG on enterprise carbon emission intensity is mainly attributed to enhanced corporate profitability, which effectively decelerates the growth rate of enterprise carbon emissions, albeit insufficient to arrest the overall increase. This observation points to a certain degree of “green paradox” phenomenon. Overall, the analysis underscores the significant contribution of ESG in promoting enterprises’ green transformation efforts.

How Does Green Finance Influence Carbon Emission Intensity? A Non-Linear fsQCA-ANN Approach

How Does Green Finance Influence Carbon Emission Intensity? A Non-Linear fsQCA-ANN Approach

Research and Suggestions on Improving the Carbon Emission Management Capacity of Coal-fired Power Plants

This paper discusses how coal-fired power plants can improve their carbon emissions management capabilities in the context of the 2030 carbon peak policy. The article first outlines the current status of implementing carbon peak policies in China’s energy sector, analyzes the current status of carbon emissions from coal-fired power plants, including carbon emission intensity, progress in low-carbon transformation, and the operation of the carbon emissions trading market. Subsequently, the article proposes six suggestions for improving the carbon emissions management capabilities of coal-fired power plants, and provides typical cases for each suggestion. Finally, the article looks forward to the challenges and opportunities faced by coal-fired power plants in carbon emissions management in the future, and emphasizes the importance of improving carbon emissions management capabilities for coal-fired power plants.

The impact of AI on enterprise energy management: from the perspective of carbon emissions

Given the significant impact of artificial intelligence (AI) technology on corporate energy management and the lack of research in this area, this paper employs text mining techniques to objectively assess the relative level of AI adoption among Chinese listed companies. Using econometric modelling methods, we verify these hypotheses and investigate both the direct and indirect effects of AI on corporate carbon emission intensity. our research finds that the carbon emission intensity of Chinese enterprises significantly decreased in the early stage, then stabilized, and has notably decreased again in recent years. The average level of AI among listed Chinese enterprises shows an overall upward trend, but the growth rate has slowed down. The level of AI in private enterprises is significantly higher than that in other types of enterprises, while the level of AI in state-owned enterprises is relatively lower. The level of AI in enterprises has a significant negative impact on carbon emission intensity, presenting an “S”-shaped relationship, characterized by initial emission reduction, mid-term rebound, and subsequent emission reduction. AI technology reduces the level of carbon emissions in enterprises by enhancing their green development standards and promoting technological innovation. There are significant differences in the impact of AI levels on carbon emission intensity across different types and regions of enterprises. The empirical conclusions remain robust after addressing endogeneity issues or variable substitution. This study provides important insights for corporate energy transitions and sustainable development, as well as for the formulation of government energy policies.

The Role of Low-Carbon Fuels and Carbon Capture in Decarbonizing the U.S. Clinker Manufacturing for Cement Production: CO2 Emissions Reduction Potentials

Low-carbon fuels, feedstocks, and energy sources can play a vital role in the decarbonization of clinker production in cement manufacturing. Fuel switching with renewable natural gas, green hydrogen, and biomass can provide a low-carbon energy source for the high-temperature process heat during the pyroprocessing steps of clinker production. However, up to 60% of CO2 emissions from clinker production are attributable to process-related CO2 emissions, which will need the simultaneous implementation of other decarbonization technologies, such as carbon capture. To evaluate the potential of fuel switching and carbon capture technologies in decarbonizing the cement industry, a study of the facility-level CO2 emissions is necessary. This study evaluates the potential for using a single low-carbon fuel as an energy source in clinker production for cement manufacturing compared to conventional clinker production (which uses a range of fuel mixes). In addition, conventional carbon capture (operated with natural gas-based steam for solvent regeneration) and electrified carbon capture configurations were designed and assessed for net-zero emission targets. Carbon emissions reductions with and without biogenic emissions credits were analyzed to ascertain their impact on the overall carbon accounting. Results show that carbon emissions intensity of cement can vary from 571 to 784 kgCO2eq/metric ton of cement without carbon capture and from 166.33 to 438.66 kgCO2eq/metric ton of cement with carbon capture. We find that when biogenic carbon credits are considered, cement production with a sustainably grown biomass as fuel source coupled with conventional carbon capture can lead to a net-negative emission cement (−271 kgCO2eq/metric ton of cement), outperforming an electrified capture design (35 kgCO2eq/metric ton of cement). The carbon accounting for the Scope 1, 2, and biogenic emissions conducted in this study is aimed at helping researchers and industry partners in the cement and concrete sector make an informed decision on the choice of fuel and decarbonization strategy to adopt.