To reveal the regional differentiation characteristics of carbon emissions during the construction phase of expressways and to improve prediction accuracy, six typical expressway projects located in the plain, hilly, and mountainous regions of Anhui Province were selected as case studies. A carbon emission accounting model for the construction phase was established based on the life cycle assessment method, and the effects of the bridge-tunnel ratio, subproject structure, and material and energy consumption on carbon emission intensity were systematically analyzed. On this basis, a regional carbon emission prediction model was developed and optimized using data from 21 completed expressways across the province. The results indicate that carbon emission intensity exhibits a significant topographic gradient, with mountainous regions showing higher values than hilly regions, and hilly regions higher than plain regions. The maximum carbon emission intensity in mountainous projects reaches 5.27 × 10⁷ kg CO₂/km, which is 2.86 times that of plain regions. As terrain complexity increases, the carbon emission structure shifts from being dominated by subgrade engineering and interchange engineering to being dominated by structural engineering, such as bridges and tunnels. In mountainous regions, emissions from structural engineering account for more than 50% of the total emissions. At the material level, cement and steel are identified as the primary emission sources, jointly accounting for 78% of total emissions in mountainous projects, and demonstrating the highest sensitivity to variations in total emissions. The prediction results show that the baseline model using the bridge-tunnel ratio as a single variable achieves a coefficient of determination (R²) of 0.69. After incorporating material and energy consumption variables, the optimized XGBoost model improves the coefficient of determination to 0.9517, achieving high-accuracy prediction using only eight categories of material and energy consumption indicators. Based on the analytical results, differentiated emission reduction pathways are proposed. In mountainous regions, priority should be given to optimizing the design of tunnels and interchange engineering and controlling the intensity of high-carbon structural materials. In plain and hilly regions, emphasis should be placed on low-carbon design and construction optimization of bridge and culvert engineering and subgrade engineering. This study provides a data-driven basis for regional carbon emission prediction and emission reduction decision-making during the construction phase of expressways.

ABSTRACT This study examines the impact of executives’ IT background on firm-level carbon emissions using Chinese data. Empirical findings indicate that a one-standard-deviation rise in the presence of IT-background executives corresponds to a 5.53% reduction in carbon emission intensity. We confirm the robustness of this finding through various robustness checks. The influence of IT-background executives on corporate carbon emission behavior operates largely through two channels: energy consumption and green innovation. The effect is especially pronounced in firms that exhibit weaker internal governance, face higher financial constraints, have lower product competitiveness, or operate in more market-oriented regions. This research shows how IT-background executives leverage technology ethically for emission reduction.

Introduction Against the backdrop of China’s comprehensive push toward the “dual carbon” goals, local governments are expected not only to articulate green development commitments in annual work reports, but also to translate those commitments into concrete action through environmental governance inputs. Yet policy rhetoric and governance inputs do not always move in tandem, and whether such rhetoric–action divergence affects firms’ abatement behavior remains underexplored. Methods Using matched city–firm data from China for 2008–2015, this paper employs a two-way fixed effects model to examine how divergence between local governments’ environmental rhetoric and policy action affects firms’ carbon emission intensity and through which channels. Results We find that greater rhetoric–action divergence significantly increases firms’ carbon emission intensity: a one-standard-deviation increase in the gap raises carbon emission intensity by 3.4%. Mechanism analysis shows that this effect mainly operates through three channels: weakened city-level innovation capacity, reduced investment attraction, and lower environmental regulatory intensity. Heterogeneity analysis further indicates that the effect is more pronounced for state-owned enterprises and foreign-invested firms. Reducing the rhetoric–action gap generates sizable abatement gains and remains net beneficial even after accounting for the associated fiscal costs. Discussion Overall, the paper identifies rhetoric–action consistency as a distinct and underexplored dimension of local green governance quality. Strengthening such consistency can reinforce firms’ incentives to reduce emissions and allow environmental governance to deliver positive net gains under real fiscal constraints.

Revealing the impact of socio-economic variables on agricultural carbon emission intensity using interpretable machine learning

Given that the construction industry is a major source of energy consumption and carbon emissions, its green transition holds significant implications for both economic development and environmental sustainability. This study takes the digital economy as its point of departure and systematically examines its impact on the carbon emission intensity in the construction industry, as well as underlying transmission mechanisms. Based on theoretical analysis, this study employs fixed-effects and mediation-effects models for empirical testing. The study finds that the digital economy can significantly reduce the carbon emission intensity of the construction industry, a conclusion that remains robust after a series of robustness tests. Mechanism analysis shows that industrial structure optimization plays an important mediating role in the process through which the digital economy promotes the reduction of carbon emission intensity in the construction industry. Heterogeneity analysis indicates that the impact of the digital economy on the carbon intensity in the construction industry is greater in the central and western regions than in the eastern regions. However, this impact is relatively smaller in regions with high urbanization levels than in those with average urbanization levels. At the same time, the impact of the digital economy on the carbon intensity in the construction industry is greater in low-income regions than in high-income regions. Finally, the study proposes measures to reduce the carbon emission intensity of the construction industry.

Global climate change necessitates urgent carbon reduction, with the building sector being a major contributor. This study conducts a comprehensive life cycle carbon emission analysis of a nearly zero-energy office building in Shenyang, China, using the LCA theory and the carbon emission factor method. The calculation covers the production and transportation of building materials, construction, operation, and demolition stages. The results show that the building’s average annual carbon emission intensity is 56.36 kgCO2e/(m2·a). The operation stage contributes the largest share, with an intensity of 37.83 kgCO2e/(m2·a), primarily due to HVAC energy consumption. The material production and transportation stage follows, accounting for 31.67% of total emissions. Compared to conventional buildings, the proportion of operational emissions in this nearly zero-energy building is relatively lower, while the share from material production is significantly higher due to the use of high-performance insulation and components. Based on these findings, targeted carbon reduction strategies are proposed for each life cycle stage, emphasizing low-carbon material selection, renewable energy utilization, and efficient design. This study provides a quantitative reference for achieving carbon reduction goals in the building sector.

Purpose The carbon emission intensity of tunnel projects is highly dependent on design schemes, making carbon management during the design phase a challenge. Although existing research on design-oriented carbon emission management has made notable progress, it remains fragmented and lacks a systematic overview. This article reviews current studies, identifies major research gaps and proposes improvement pathways to support tunnel decarbonization. Design/methodology/approach This study uses a systematic literature review following PRISMA guidelines to analyze 60 core publications on carbon emission management in tunnel design. Findings Current research on carbon emission management in tunnel engineering design focuses on assessment methods, low-carbon design elements, optimization of carbon emissions in design schemes and emission reduction strategies. However, it identifies four major limitations: a lack of uncertainty analysis in assessment methods, insufficient research on the coupling mechanism of low-carbon design elements, absence of dynamic optimization for the entire lifecycle and weak adaptability of emerging technologies. To address these challenges, a new framework for carbon emission management in tunnel engineering design is proposed, based on “intelligent perception-regulation optimization-negative carbon conversion.” Originality/value The study proposes a carbon emission management framework for tunnel engineering design based on “intelligent perception-regulation optimization-negative carbon conversion,” not only fills structural gaps in the theoretical system of low-carbon design for tunnel engineering but also provides practical decision-making tools for engineering practice, enabling a negative growth in total carbon emissions while maintaining engineering efficiency.

Potential forces for low-carbon transition: The role of analyst attention in China's high-polluting industries.

ABSTRACT There has been interest in improving supplier environmental performance, with past literature examining the influence of buyers and competitors on supplier environmental performance separately. However, examining the role of these two stakeholders in tandem remains underexplored, representing a critical shortcoming as it reflects contemporary competitive dynamics. Additionally, much past literature has used “shared industry” to identify competitors instead of directly identified relationships. Using a panel dataset of directly identified buyer and competitor relationships, this research employs a stakeholder theory perspective to examine how suppliers respond to underperforming (generating more) and overperforming (generating less) buyers and competitors in terms of carbon emissions intensity, an important dimension of environmental performance. The results suggest that suppliers are responsive to underperformance relative to buyers and competitors, but only until their carbon emissions intensity is below that of stakeholder organizations. While the response is stronger for underperformance relative to buyers, competitors still have influence. Similarly, suppliers have worse environmental performance the year after overperforming buyers or competitors, with buyers having a stronger influence. Furthermore, we find market competitiveness plays a moderating role, as suppliers are more responsive in competitive markets. Post hoc analyses extend these examinations using additional sustainability outcomes.

Physics-informed graph neural network for carbon emission flow analysis in power systems

City positionality in supply chain network and urban carbon emission intensity: Evidence from listed companies in China

This study examines the coupling coordination between energy consumption and carbon emissions in China’s agricultural sector and explores its implications for the low carbon transition. Using a coupling coordination model and provincial panel data from 2012 to 2021, the study assesses the level of energy carbon coordination in China’s agricultural sector and analyzes its key determinants. Fixed effects models evaluate the impact of terminal energy structure, while mediation analysis tests whether agricultural carbon emission intensity transmits these effects. Additionally, out of sample validation and short term scenario projections are used to examine temporal stability. The results show that energy carbon coordination in China’s agricultural sector is generally at a moderate to good level, indicating progress toward green transformation, although substantial regional disparities remain. Terminal energy structure is a key determinant, higher coal and diesel shares significantly reduce coordination, whereas a higher natural gas share improves it. Mediation analysis further shows that agricultural carbon emission intensity partly transmits these effects. Regional heterogeneity is observed, low GDP provinces are mainly constrained by coal dependence, while high GDP provinces face stronger diesel and electricity related emission pressures. The out of sample validation and short term scenario projections provide additional support for the temporal stability of these findings. These findings highlight the need for region specific decarbonization strategies in China’s agricultural sector, including accelerated clean energy substitution, power sector decarbonization, stronger carbon monitoring, and improved cross sector policy coordination.

The National Forest City policy in China is inextricably linked to the country’s low-carbon development and its economic and social green transformation. In this paper, the policy is employed as a quasi-natural experiment to examine the impact of Forest City Construction (FCC) on carbon emission intensity. This paper uses the statistical yearbook data published on the official website of the government, based on the carbon emission intensity of 268 cities in China in the 19 years from 2003 to 2021, and uses the multi period double difference (DID) model for analysis. The findings of the study indicate that: FFC significantly reduces urban carbon emission intensity by increasing the level of green living of residents, the level of green production of the society, and the level of green innovation, and its effect is heterogeneous at the level of geography and urban development status, and temperature and precipitation, as well as the level of urban informatization and the rationality of industrial structure, all have a positive moderating effect on it. The results of this paper show that urban forest construction significantly reduces the intensity of urban carbon emissions and improves the environmental benefits of economic construction. Compared with the existing studies focusing on a single path of emission reduction, there is a lack of systematic integration of multiple mechanisms such as “direct carbon sequestration, indirect transformation and policy coordination”. The innovation of this paper is to focus on the multi-dimensional environmental benefits brought by forest city construction, summarize the existing literature research focus, innovatively establish the three-dimensional impact mechanism of production, life and innovation, make up for the deficiency of existing studies paying insufficient attention to the multi-path synergy effect, build a more comprehensive and systematic analysis framework, and provide a new perspective and ideas for the study of low-carbon city construction.

As green development gradually gains momentum and global climate governance accelerates, pricing climate transition risks has become an important issue. Based on data from Chinese listed companies from 2010 to 2020, this paper constructs indicators for corporate carbon emission intensity and climate transition risk exposure. Using Fama-MacBeth regression and constructing carbon risk factors, it tests the existence of the “carbon premium” in the stock market and discusses its influencing factors and transmission mechanisms. The research findings are as follows: First, the stock market exhibits a significant “negative carbon premium”. Second, the “carbon premium” is influenced by industry differences, being more pronounced in high-carbon industries. To improve market pricing efficiency and guide effective capital allocation, this paper recommends that the government improve the standardized climate information disclosure system.

The digital economy has emerged as a pivotal driver of China’s green and low-carbon transformation; however, its specific transmission mechanisms and spatiotemporal heterogeneity regarding environmental effects remain insufficiently explored. Utilizing panel data from 30 Chinese provinces (2011–2020), this study employs mediation analysis and Geographically and Temporally Weighted Regression (GTWR) to estimate the impact of the digital economy on pollution and carbon emissions. The results indicate that: (1) The expansion of the digital economy significantly mitigates environmental pressures, reducing pollutant and carbon emission intensities by 0.247 and 0.258 units per unit of growth, respectively. (2) Transmission mechanism analysis reveals that technological innovation is the primary channel, mediating 40.01% of the total effect, significantly outweighing the contribution of industrial structure optimization (16.85%). (3) The effects are spatially heterogeneous, with stronger pollution reductions in northeastern and eastern provinces and larger carbon-intensity reductions in western provinces. (4) The synergistic effects strengthened from 2016 to 2020. The earlier spatial misalignment between pollution control and carbon mitigation was effectively eliminated.

Introduction Global warming is progressing, and China is under great pressure to reduce carbon dioxide emissions. The three northeastern provinces belong to the country’s powerful industry regions. They not only produce a large amount of carbon emissions but also suffer from city shrinkage. These two facts strongly indicate that China should progress toward low-carbon development. Methods Using data from the province-level cities in the three northeastern provinces in 2005, 2010, 2015, 2020, and 2023, this study explores their city shrinkage and carbon emission intensity spatial and time frames, and further explores how urban shrinkage affects the mechanisms of carbon emission intensity. Results Results show that core cities have little contraction and some are even still growing, while edge cities face stronger contraction. Over time, the overall level of contraction has gradually weakened. From 2005 to 2023, the average carbon emission intensity decreased from 4.54 to 2.87, with the standard deviation narrowing from 3.25 to 1.86. Regions with higher economic development generally showed a downward trend in carbon emission intensity, while resource-based cities showed more fluctuations. The relationship between urban shrinkage and carbon emission intensity in the three northeastern provinces improved each year but differed across regions, presenting a spatial pattern of high coordination in core cities and low coordination in peripheral areas. Among the influencing factors, GDP had a clear positive effect on carbon emission intensity with the highest explanatory power ( q = 0.404). The shares of human capital and per capita road area showed big spatial and temporal differences in their effects on carbon emissions, where the inhibitory effect of human capital gradually diminished and the promoting effect of road area intensified. Discussion This study provides theoretical references and decision-making support for formulating differentiated low-carbon planning strategies and achieving high-quality revitalization in old industrial bases facing urban shrinkage.

Against the backdrop of China’s “Digital China” initiative and “dual carbon” goals, this study examines the impact of digital transformation on carbon emission reduction in Chinese A-share listed manufacturing firms from 2011 to 2022. Using a three-way fixed effects model, we find that digital transformation significantly reduces corporate carbon emission intensity, and this result remains robust after endogeneity treatment and a series of robustness checks. The carbon reduction effect is realized through two core mediating paths: improving resource allocation efficiency and promoting green technology innovation. Further analysis shows that carbon disclosure quality positively moderates the above relationship, and the carbon reduction effect of digital transformation is heterogeneous across firm ownership, regional distribution, and pollution attributes. This study provides empirical evidence for the coordinated digital and green transformation of China’s manufacturing sector.

Under the impetus of the dual carbon strategy and the burgeoning digital economy, Chinese enterprises have been rapidly embracing digital transformation, with the adoption rate of digital technologies reaching 47.4% by 2023. Nevertheless, the nuanced interplay between corporate digitalization and carbon emission intensity remains insufficiently understood. Employing panel data from China's A-share listed firms spanning 2010 to 2022, this study systematically investigates how corporate digital transformation shapes carbon emission intensity. Empirical results demonstrate that digital transformation substantially curtails corporate carbon emission intensity, a finding that withstands a battery of robustness checks including the Heckman two-stage correction and propensity score matching. Mechanism analysis further reveals that digital transformation attenuates carbon emission intensity primarily through augmenting R&D expenditure. Heterogeneity tests indicate that this mitigating effect is particularly salient among state-owned enterprises and firms audited by Big Four accounting firms. These findings not only advance scholarly understanding of the determinants of corporate carbon emission intensity but also offer practical implications for leveraging digitalization to foster low-carbon development and the realization of China's dual carbon objectives.

Accurately identifying the spatiotemporal evolution and spatial differentiation of carbon emission intensity in the transport sector is essential for formulating region-specific carbon reduction policies. This study develops an analytical framework that integrates both static and dynamic perspectives to examine spatial disparities in transport sector carbon emission intensity. From a static perspective, the Dagum Gini coefficient is employed to quantify spatial differences and their sources of transport carbon emission intensity. From a dynamic perspective, kernel density estimation is applied to depict the evolution trajectories of transport carbon emission intensity. Furthermore, the traditional Markov chain model is refined to construct a spatial Markov chain model that accounts for spatial adjacency, enabling identification of persistence and spatial spillover effects. The empirical results indicate that (1) The carbon emission intensity of the transport sector in China presents an overall declining trend with significant spatial heterogeneity among provinces. Regional disparities have expanded, with the largest gap between the eastern and western regions, where inter-regional differences contribute an average of 47.374% to total disparity, representing the main source of variation. (2) The carbon emission intensity in the national, eastern, and central regions tends to converge gradually, while the western region shows a pattern of initial convergence followed by renewed divergence. Within each region, several provinces maintain carbon emission intensity levels significantly higher than the average, forming a clear spatial gradient structure. (3) The traditional Markov chain analysis reveals evident persistence and club convergence in transport carbon emission intensity. The spatial Markov chain analysis further shows that neighboring regions strongly influence local transition probabilities, demonstrating spatial spillover and path dependence effects. Hypothesis testing confirms the necessity of incorporating spatial dependence into the analysis. Based on these findings, this study proposes that carbon reduction strategies in the transport sector should be tailored to regional disparities and spatial interdependencies, aiming to enhance overall mitigation efficiency and foster coordinated governance.

Summary Achieving low-carbon development has become a key goal for countries and regions worldwide. The new industry–finance cooperation (IFC) model emerging in China holds promise as an effective approach to reducing carbon emission intensity (CEI). This paper utilizes data from 260 Chinese cities from between 2011 and 2022 (n = 260, 12 years), employing a difference-in-differences model to examine the impact of IFC on urban CEI. The IFC appears to have helped reduce urban CEI, potentially through promoting green technological innovation, advancing technological finance and talent gathering. Furthermore, the effect of IFC on reducing urban CEI varied depending on resource endowment, regional characteristics and prevailing CEI. Overall, the impact of IFC on CEI has not been significant in resource-dependent cities (highly dependent on the extraction and primary processing of one or two resources, such as coal and oil, for which the industrial structure is extremely narrow); however, for resource-dependent cities undergoing transformation (e.g., declining or regenerating), IFC seems to have had a significant carbon reduction impact. Our findings stand to inform governments, financial institutions and industrial organizations worldwide in their joint efforts to achieve low-carbon emission targets.