Carbon Emission Intensity Research Articles

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.

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

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.

Correction: The impact of green bond issuance on carbon emission intensity and path analysis

Correction: The impact of green bond issuance on carbon emission intensity and path analysis

Do policies make a difference? Assessing the impact of China's air pollution prevention and control action plan on carbon emissions

This study aims to balance the dual objectives of improving environmental quality and achieving carbon peaking and carbon neutrality goals. Utilizing panel data from 274 Chinese cities between 2006 and 2020, this paper employs the difference-in-differences method to examine the impact of the Air Pollution Prevention and Control Action Plan (APPCAP) on urban carbon emissions. Empirical results indicate that the impact of the APPCAP on regional carbon emissions follows an inverted U-shaped curve, exhibiting a dual impact path. Although the implementation of the APPCAP increased the annual carbon emissions of the city by an average of 1.637 million tons, it showed a trend of inhibiting the growth of carbon emissions, although it was not significant. Specifically, the APPCAP is most effective in reducing carbon emissions during the "source prevention" stage, primarily by increasing industrial electricity consumption and reducing coal production and consumption. However, a rebound effect occurs at the policy period's end, with compensatory increases in crude oil and electricity consumption, thereby elevating regional carbon emission levels. The APPCAP's effect on carbon emissions is more pronounced in areas with lower population density and higher environmental regulation intensity. It notably raises carbon emission levels in non-industrial base and non-resource-based cities compared to other cities. Furthermore, the APPCAP positively impacts regional carbon emission intensity and economic growth, significantly driving the secondary industry while minimally affecting the primary industry.

Decomposition of Carbon Dioxide (CO2) Emissions in Hungary

Elevated levels of global greenhouse gases (GHGs), primarily produced through fossil fuel combustion, present environmental risks such as prolonged droughts, global warming, and catastrophic floods. Despite raising awareness and international efforts to combat climate change, fossil fuel and GHGs demand persists. At this juncture, this study investigates Hungary's transportation sector, a notable CO2 emitter. Using the Kaya Identity and Log Mean Divisia Index method, this study decomposes the primary components of CO2 emissions in Hungary's transport sectors from 2001-2021. This analysis is done in two decomposition levels, annual and periodic decomposition analysis for each of the five years starting from 2001. The analysis reveals that the economic activity effect ∆CGPE is the predominant driver of CO2 emissions, while the energy intensity effect ∆CEIE improvements have substantially mitigated these increases. Population changes affect ∆CPOPE, and the carbon emission intensity effect ∆CCEIE has had minimal impacts. The study provides insights and recommendations for policy and strategic interventions to reduce emissions and promote sustainable transportation practices in Hungary.

County-level carbon emissions in the guanzhong area of Shaanxi province: towards achieving China’s dual carbon goals

China’s rapid urbanization has significantly impacted carbon emissions in rural areas, driving the need for region-specific carbon management strategies to achieve the country’s dual carbon goals. However, previous research has primarily focused on large urban centers, leaving a gap in understanding the spatial and temporal patterns of carbon emissions at the county level in rural regions. This study focuses on the Guanzhong region of Shaanxi Province and develops a county-level carbon emission accounting system based on geographic, socio-economic, and land-use data. The carbon emissions are categorized into energy, industrial processes, agriculture, and waste management. Key findings indicate that industrial and residential sectors are the primary contributors to carbon emissions, with Xi’an being the largest emitter. Carbon emission intensity follows a ranking of Xi’an < Weinan < Baoji < Xianyang < Tongchuan. Spatial patterns show a “core-edge” distribution, with higher emissions in urban centers and lower emissions in rural areas. The study also highlights the carbon sink potential in the southern Qinling National Park. This research provides a valuable framework for rural low-carbon development and offers critical insights for policymakers aiming to balance carbon reduction and economic growth in rural China.

Temporal Evolution Pathway and Forecasting of Non‐Fossil Energy Consumption and Carbon Emission Under China's Carbon Peak Target: A Markov Switching AR and RNN Approach

ABSTRACTTo fulfil the commitments of the Paris Agreement, China will strive to achieve carbon peak (CP) by 2030. It is necessary to identify the evolution characteristics of China's carbon emissions and provide a scientific path prediction for the formulation of reasonable emission reduction policies and measures. This study summarises and predicts the pathway of China's carbon peak (CP) using carbon emission intensity (CEI) and the percentage of non‐fossil energy consumption (NEC) as indicators, and combining MSIH(3)‐AR(2) model and recurrent neural network. The results show that: (1) China's CEI experiences a ‘low decline regime’ (LDR), a ‘medium decline regime’ (MDR) and a ‘high decline regime’ (HDR), while the share of NEC goes through a ‘low fluctuation regime’ (LFR), a ‘medium growth regime’ (MGR) and a ‘high growth regime’ (HGR). (2) For CEI, the switching probability from MDR to the HDR is 74.88%, illustrating a substantial improvement. For NEC, the switching probability from MGR to HGR is 28.92%, but the probability of returning to MGR is 61.76%, indicating an adjustment. (3) By 2030, CEI will reach 0.9896 tons/100 million CNY, decreased by 66.35% compared with 2005. While the percentage of NEC will rise to 26.61%. Based on these, policy suggestions such as strengthening the top‐level design, upgrading energy mix and accelerating green technological changes are proposed to break the bottlenecks of reaching CP and further zero carbon goal. This study is expected to provide theoretical support and empirical evidence for the achievement CP in China, and to provide empirical references for promoting the ‘dual carbon’ process in other countries.

Impact and mechanisms of high-speed rail construction on carbon emissions: A quasi-natural experiment in China

Carbon emissions present a significant climate challenge for China. As a major source of these emissions, reducing transportation-related carbon output is essential to achieving the country's dual carbon goals. In this context, high-speed rail (HSR) emerges as a green, low-carbon alternative with an increasingly significant role in reducing carbon emissions. This paper explores the impact of HSR station construction on carbon emissions using data from 248 prefecture-level cities from 2003 to 2019, treating the construction as a quasi-natural experiment. Employing a spatial DID model, the research investigates how HSR construction affects carbon emission intensity within the region and in neighboring areas. It also considers socioeconomic factors to understand their mediating roles. The findings reveal that HSR construction significantly reduces regional carbon emissions over time, with long-term and gradually increasing effects. Spatially, HSR also has substantial spillover effects in reducing carbon emissions in adjacent regions. Numerically, HSR reduces carbon emissions by an average of 1.7% in the local region and 2.3% in the surrounding areas. The mechanism analysis indicates that the carbon reduction benefits of HSR stem from a complex interplay of multiple factors, with each selected factor playing a partial mediating role. Notably, the concentration of human capital and the flow of innovation are crucial pathways for regional carbon reduction. However, despite HSR's promotion of industrial structure upgrades and substitution of traditional transportation, these factors have not yet significantly contributed to carbon reduction under current conditions. These results underscore the critical position of HSR in carbon reduction and provide a theoretical foundation for future HSR planning and sustainable development policy formulation.

Stronger Impact of Extreme Heat Event on Vegetation Temperature Sensitivity under Future Scenarios with High-Emission Intensity

Vegetation temperature sensitivity is a key indicator to understand the response of vegetation to temperature changes and predict potential shifts in ecosystem functions. However, under the context of global warming, the impact of future extreme heat events on vegetation temperature sensitivity remains poorly understood. Such research is crucial for predicting the dynamic changes in terrestrial ecosystem structure and function. To address this issue, we utilized historical (1850–2014) and future (2015–2100) simulation data derived from CMIP6 models to explore the spatiotemporal dynamics of vegetation temperature sensitivity under different carbon emission scenarios. Moreover, we employed correlation analysis to assess the impact of extreme heat events on vegetation temperature sensitivity. The results indicate that vegetation temperature sensitivity exhibited a declining trend in the historical period but yielded an increasing trend under the SSP245 and SSP585 scenarios. The increasing trend under the SSP245 scenario was less pronounced than that under the SSP585 scenario. By contrast, vegetation temperature sensitivity exhibited an upward trend until 2080 and it began to decline after 2080 under the SSP126 scenario. For all the three future scenarios, the regions with high vegetation temperature sensitivity were predominantly located in high latitudes of the Northern Hemisphere, the Tibetan Plateau, and tropical forests. In addition, the impact of extreme heat events on vegetation temperature sensitivity was intensified with increasing carbon emission intensity, particularly in the boreal forests and Siberian permafrost. These findings provide important insights and offer a theoretical basis and guidance to identify climatically sensitive areas under global climate change.

Assessing the Sustainability Impact of Land-Use Changes and Carbon Emission Intensity in the Loess Plateau

Assessing the Sustainability Impact of Land-Use Changes and Carbon Emission Intensity in the Loess Plateau

Do China's ecological civilization advance demonstration zones inhibit fisheries' carbon emission intensity? A quasi-natural experiment using double machine learning and spatial difference-in-differences

China's National Ecological Civilization Demonstration Zone (NECDZ) policy has a significant role in ensuring national ecological security, and it is essential to investigate how the NECDZ policy affects the carbon emissions intensity of fisheries (CEIF) to advance China's commitment to reducing carbon emissions. This study evaluates the CEIF in 30 Chinese provinces from 2007 to 2021 using ecological civilization demonstration areas as a quasi-natural experiment and double machine learning (DML)to examine the impact and internal mechanisms of NECDZ implementation on the CEIF. We also explore spatial spillover effects using a spatial difference-in-differences approach. The results reveal that NECDZ implementation has a significantly negative impact on the CEIF and this effect continues over time. NECDZ policy potentially affects the CEIF through technology development, industrial structure improvement, and reduced energy consumption. Further investigation reveals that NECDZ implementation has spillover effects and inhibits the CEIF in surrounding regions. Therefore, it is essential to focus on developing the NECDZ policy to enhance fisheries' industrial structure, encourage low-carbon innovation in fishery technologies, and increase energy consumption efficiency. This could be supported by facilitating exchanges and cooperation with other areas, considering regional disparities, and assigning common but distinct responsibility for reducing the CEIF.

Digital economy, technological progress, and carbon emissions in Chinese provinces

The 2023 work report of the Chinese government underscores a pivotal transition towards a “dual-control” policy, prioritizing the management of carbon emissions. This study provides an in-depth analysis of the interplay between the digital economy, technological progress, and their impact on the total volume and intensity of carbon emissions across 30 Chinese provinces from 2013 to 2021. Our findings reveal that while the expansion of the digital economy and technological progress contribute to an increase in the total carbon emissions, they also markedly decrease carbon intensity, paving the way for sustainability. Additionally, the research uncovers the positive externalities of the digital economy on carbon emission intensity and the spillover effects of technological progress on both emissions and intensity. The integration of the digital economy in industrial restructuring and the uptake of green technologies are identified as instrumental in mitigating carbon emissions. These insights underscore the potential of policy strategies that leverage the digital economy and technological innovation to meet the “dual-control” policy objectives and foster sustainable development.

Synergistic enhancement for energy-saving, emission reduction and profit improvement in iron and steel manufacturing system: Strategies for parameter regulation and technologies integration

The steel industry, characterized by high energy consumption and carbon emission, poses a significant threat to global energy and environmental security. In response to the energy conservation and dual-carbon targets, the steel industry must urgently find sustainable pathways for transformation and development. Steel production structure optimization and the application of cutting-edge technologies have been proven to be highly effective. However, the complexity and variability of actual production conditions highlight the absence of a methodology capable of adapting to real production scenarios, elucidating influencing principles across multiple factors, analyzing synergistic mechanisms of multiple indicators, and proposing collaborative control strategies. Furthermore, the potential for technology applications that are closely integrated with the company’s resource endowment remains uncertain, leading to an unclear overall strategy for energy saving and carbon reduction. This study develops a multi-scale optimization and evaluation model for ISMP, incorporating interconnected and matching processes, as well as nested multi-indicators, in response to the context. Subsequently, the disturbance mechanisms of various factors are accurately identified and a multi-indicator synergistic control strategy and framework are proposed. Following optimization, the exergy loss, energy consumption, carbon emission intensity, cost, and pollutant emissions of ISMP per tonne of steel are reduced by 1418.70 MJ, 42.93 kgce, 154.79 kg, 171.21 CNY, and 0.0168 kg, respectively. Additionally, measures such as reducing moisture content in coking coal can effectively regulate multiple indicators. Based on the proposed sensitivity quantification method, the temperature of hot blast is identified as the most effective controllable factor. Technologies like hydrogen-rich fuel injection, integrated waste heat recovery, and solar photovoltaic & energy storage have shown favorable prospects, according to analysis conducted from various angles regarding economic viability and potential for energy-saving and carbon reduction. However, the application of aforementioned technologies may be limited by financial consideration, necessitating cautious and judicious investment choices.

The synergetic competition of cross-regional integrated energy systems with low-carbon technology heterogeneity under carbon emission trading mechanism to achieve carbon reduction target

The integration of renewable and fossil energy in a Region Integrated Energy System (RIES) is recognized as an effective measure for carbon emission reduction. The emergence of carbon emission trading (CET) markets worldwide has raised the need to combine RIES optimal planning with CET to achieve carbon reduction targets. This study focuses on the synergetic competition of a cross-RIES system with low-carbon technology heterogeneity under the CET mechanism. A cross-RIES model is constructed, incorporating fossil Carbon Capture System (fossil-CCS), renewable Combined Cooling Heating and Power (renewable-CCHP), and cleaner production paradigms. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to propose a bi-level multi-objective optimal planning framework for the cross-RIES model, employing a grandfather permit allocation method. Comparative analysis is conducted between cross-RIES optimal planning with and without considering the CET mechanism, with a focus on environmental indexes such as carbon emission, carbon intensity, and marginal abatement cost. Sensitivity studies are also performed to explore the impact of key CET design parameters on the system's environmental performance. The simulation results demonstrate that the CET mechanism effectively reduces carbon emission, primary energy consumption, and carbon intensity by promoting synergetic competition among the low-carbon technology heterogeneity of RIESs. Furthermore, the environmental performance varies among the three types of RIES. In terms of CET parameter design, lowering the cap constraint, reducing the allowance allocation ratio of fossil-CCS RIES, and increasing the trading price contribute significantly to reducing carbon emission and carbon intensity, albeit at the expense of higher marginal abatement cost.

Decoupling effect and influencing factors of carbon emissions in China: Based on production, consumption, and income responsibilities

Carbon emissions at the provincial level are mostly calculated based on production responsibility, which ignores the carbon emission accounting perspectives of consumption and income responsibility. However, scientific accounting of carbon emissions from multiple perspectives is crucial to equitably distribute the responsibilities for reducing emissions. This study utilises an input‒output model for multi-regional carbon emissions to calculate carbon emissions from production-, consumption- and income-based perspectives for exploring the variability in the responsibilities for reducing emissions at the provincial level. The Tapio decoupling coefficient is used to analyse the relationship between carbon emissions and economic growth in key provinces, and the Logarithmic Mean Divisia Index (LMDI) model is used to determine the factors promoting or inhibiting the decoupling of carbon emissions under different responsibility perspectives. The results show that, firstly, the national carbon emissions illustrate the following trend: production-based emissions > income-based emissions > consumption-based emissions. Secondly, provinces in strong decoupling states are distributed in a northeast–southwest direction. Carbon emissions and decoupling situations differ among varying responsibility perspectives due to interprovincial transfers of carbon emissions. Thus, a compensation mechanism should be established to promote a fair and orderly peaking in each province. Lastly, carbon emission intensity is a factor promoting decoupling in all three perspectives. In the meantime, output scale, economic development and factor input are the major inhibiting factors for the decoupling of production-, consumption- and income-based emissions, respectively.

Impacts of the sea-rail intermodal transport policy on carbon emission reduction: The China case study

China introduced the Intermodal Transport Demonstration Project Policy in 2015, spurring the rapid expansion of sea-rail intermodal transport (SRT) at coastal ports. This policy is of crucial importance as SRT is pivotal in supporting the transport sector's achievement of the “double carbon target”. Despite strong policy backing and notable growth of SRT, the impact of these SRT demonstration projects on curbing transport carbon emissions has not been comprehensively evaluated. This study addresses this gap by using panel data from hub seaports across nine provinces and applying a multi-period-continuous difference-in-differences model. The research quantifies SRT's contribution to reducing transport carbon emission reductions as a result of the policy. Key findings indicate: (1) Each 10,000 TEU increase in containerized SRT volume results in a 0.162% reduction in transport carbon emissions, with projected growth by 2025 leading to a 9.02% decrease in emissions. (2) SRT lowers transport energy consumption and carbon emission intensity, fostering a green transformation in energy use. (3) Geographical analysis indicates that policy implementation in the southern region requires further strengthening. This study enhances empirical understanding of low-carbon SRT development in hub ports, emphasizing its critical role in China's transition to decarbonized transport. It also highlights the importance of regional policy effectiveness, offering valuable insights for policymakers striving to promote sustainable transport modes.