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.

Under the background of the continuous advancement of the digital Yangtze River Delta construction and the "dual carbon" goals, the digital economy, as a new driving force integrating digitalization and low-carbon development, helps the construction industry achieve green and low-carbon transformation and high-quality development by directly reducing carbon emissions and indirectly promoting green technological innovation. Based on the data of 41 cities in the Yangtze River Delta region from 2011 to 2022, this study analyzes the current situation and spatial-temporal characteristics of carbon emissions in the construction industry and further explores the impact and mechanism of digital economy and green technological innovation on carbon emission reduction in the construction industry by combining panel data regression models and mediation effect tests. The results showed that: ① From 2011 to 2022, although the carbon emissions from the construction industry in Yangtze River Delta cities increased in a low-amplitude wave pattern and spatial agglomeration weakened, the level of digital economy development in various provinces and cities has significantly improved, with an overall cumulative increase of nearly 1.3 times, among which Anhui had the fastest growth, and most cities had achieved positive growth. ② There was a significant positive correlation between the digital economy and carbon emission intensity, and this influence remained after robustness and endogeneity tests. The impact varied with urban resource endowments, with resource-based cities showing significant effects. ③ In the context of digital economy promoting the low-carbon development of the construction industry, green technological innovation played a positive mediating role. The research results are of great significance for formulating relevant policies of digital economy in the field of construction emission reduction and provide theoretical support for achieving the goal of green and low-carbon development.

The coupling and coordination relationship between provincial carbon emissions and new quality productivity in China is a key path to achieve the "dual carbon" goals and promote high-quality development in a coordinated manner. Based on panel data from 30 provinces in China from 2012 to 2022, a coupling coordination degree model, spatial autocorrelation analysis, and β convergence model were constructed to systematically measure the dynamic coordination and regional convergence characteristics of carbon emission intensity and new quality productivity. The results showed that: ① The national coupling coordination degree increased from 0.529 to 0.664, upgrading from "barely coordinated" to "primary coordinated, " with an average annual growth rate of 2.55%. ② The spatial differentiation presented a pattern of "high in the southeast and low in the northwest, " and the global Moran index verified a significant positive spatial correlation. The coupling coordination degree H-H agglomeration area expanded from 7 to 11 provinces, reflecting the radiation effect of the Yangtze River Delta and Pearl River Delta extending to the central and western regions, while the northwest and northeast L-L agglomeration areas are still constrained by "high carbon lock-in" and ecological vulnerability. ③ There were absolute β and conditional β convergences in the coupling coordination degree, and the convergence speed of underdeveloped provinces was significantly faster than that of developed provinces. The advantage of latecomers and the diffusion effect of technology drove the narrowing of regional differences. Based on this, it is recommended to strengthen the global technology sharing network, promote industrial structure transformation through differentiation, and improve cross regional ecological compensation mechanisms to promote the coordinated transition of low-carbon development and new quality productivity.

Exploring the carbon reduction pathways of the paper industry in key regions of China is of significant importance for achieving low-carbon sustainable development in the Chinese paper industry. Taking the paper industry in Guangdong Province as the research subject, this study employs the logarithmic mean Divisia index (LMDI) method to conduct continuous and phase-wise decomposition of the factors influencing carbon emissions based on its energy consumption characteristics. The Tapio decoupling model is used to analyze the decoupling status between the output of the paper industry and carbon emissions, and it is combined with the LMDI model to construct a decoupling effort model, thereby elucidating the extent of efforts made by each influencing factor towards achieving decoupling. The results indicate that during the period from 2007 to 2022, the effect of industrial output value was the main driving factor for the increase in carbon emissions in the paper industry, while the energy intensity effect served as the primary inhibitory factor. The energy structure effect and the carbon emission intensity effect of electricity were both secondary inhibitory factors. The paper industry predominantly experienced an evolutionary path from weak decoupling to strong decoupling and then to regressive decoupling. The carbon emission intensity effect of electricity and the energy structure effect primarily manifested as weak decoupling efforts. The decoupling effort index of energy intensity remained generally consistent with the changes in the overall decoupling effort index. Continuous improvement in various factors will play a positive role in promoting decoupling of carbon emissions. Therefore, it is necessary to enhance the guidance for the reform and optimization of the paper industry structure and its green transformation and upgrading, thus facilitating its transition to a technology-intensive industry and fully achieving a strong decoupling between paper industry output and carbon emissions.

As China pursues its Dual Carbon Goals, understanding the environmental effects of the Belt and Road Initiative (BRI) is of critical importance. Employing panel data from 282 prefecture-level cities in China over the period 2003–2023, this study adopts a difference-in-differences (DID) approach to systematically assess the impact of the BRI on carbon emission intensity (CEI). The empirical results show that the BRI significantly reduces CEI in Chinese cities along its corridors, a finding that proves robust across multiple robustness checks and after addressing potential endogeneity concerns. Mechanism analysis reveals that the BRI reduces CEI by promoting industrial structure optimization, lowering energy intensity, and alleviating market fragmentation. Moderating effect tests indicate that government intervention strengthens the CEI reduction effect of the BRI. Heterogeneity analysis suggests that the CEI reduction effect is more pronounced in central-western cities, key environmental protection cities, old industrial base cities, and non-logistics hub cities.

Research on the carbon emission intensity and carbon reduction potential of prefabricated concrete structures

In response to global climate change challenges, both carbon taxes and emission intensity regulations are popular carbon pricing policies implemented in countries worldwide. These two carbon policies may impact industries differently, specifically the electricity and manufacturing sectors. This paper aims to investigate the optimal response of two industrial sectors to these carbon policies. The economic model is first calibrated by the cost structures, emission profiles, and market conditions typical of the two sectors. The equilibrium that captures the optimal production and abatement of firms is then given. In addition, by analysing the best response of industrial participants, insights for policymakers on effective carbon policies are provided in balancing environmental goals with economic considerations. Results reveal the significant influence of carbon pricing policies on industry profits, highlighting the need for tailored approaches for different sectors. Future research could explore the long-term effects of these policies on industry competitiveness and sustainability, informing ongoing policy development efforts.

Carbon footprint characteristics and reduction strategies of the iron and steel industry: an LCA-based study of source, process, end-use and cleaner production applications.

Revealing the impact of multi-scale innovation networks on urban carbon emission intensity: Evidence from Chinese cities

An explainable transfer learning approach to predict carbon emission intensity of coal-fired power plants with multi-source monitoring data

In order to reveal the heterogeneity characteristics and internal driving mechanism of green innovation efficiency of Chinese manufacturing enterprises under the "dual carbon" goal, this study breaks through the technical homogeneity assumption of the traditional efficiency evaluation model, and constructs a common frontier SBM-DEA model integrating non-expected output based on the panel data of manufacturing listed companies from 2015 to 2022. The dynamic action path between the technical gap and the management level is identified by the inefficiency decomposition method. The results show that: (1) the average green innovation efficiency of manufacturing enterprises in China is 0.706, with an improvement potential of 29.4%, and the efficiency gradient of low-carbon, medium-carbon, and high-carbon groups decreases, showing a step-by-step decreasing trend of carbon emission intensity and innovation efficiency. (2) The technology gap is the dominant factor in the efficiency loss of the high-carbon group, while the efficiency loss of the low-carbon group is mainly due to management inefficiency. (3) For high-carbon enterprises, strengthening environmental regulation and intellectual property protection will exacerbate their technological catch-up barriers, while industrial upgrading can narrow the frontier gap; In the low- and medium-carbon group, financing constraints inhibit management inefficiency through the backward pressure mechanism, but digital transformation has a negative impact due to resource misallocation, and ESG performance can significantly optimize the management process. This study clarifies the differentiated improvement path of green innovation from the perspective of carbon emission heterogeneity through the common frontier framework, and provides a theoretical basis for formulating collaborative policies for technical support and management intervention.

The Maritime industry faces increasing pressure to reduce greenhouse gas emissions under the International Maritime Organization’s Carbon Intensity Indicator (CII) framework. This study evaluates operational performance, fuel consumption, and carbon intensity for two aging passenger ships operated by PT. XYZ (Passenger Ship A, 34 years; Passenger Ship B, 33 years) and assesses whether repowering can improve efficiency and compliance. Financial feasibility is projected using trendline regression under two scenarios: without subsidies and with subsidies. Results indicate that without subsidies, both ships are projected to incur losses from the initial period, with deficits increasing annually. Under subsidies, Passenger Ship A’s gross profit is projected to become negative starting in 2028, while Passenger Ship B is expected to remain financially positive. Environmentally, CII results show rising carbon emission intensity, averaging annual increases of 0.56% for Passenger Ship A and 2.09% for Passenger Ship B, leading to declining CII ratings over time. Passenger Ship A is projected to reach Rating E during 2029–2035, requiring a critical operational decision by 2031, while Passenger Ship B is projected to reach Rating E during 2031–2035, requiring a decision by 2033. Repowering reduces annual fuel consumption by 41.4% and 47.5%, respectively, and improves both ships’ CII ratings to Rating A, supporting continued operation with international environmental compliance.

Operational carbon emissions of buildings account for more than 25% of global carbon emissions and, generally, over 50% of the total carbon emissions across the whole life cycle of a building. The evaluation and management of carbon emission levels during the operational phase of buildings are at present important tasks for China’s construction industry administration departments. This paper analyzes the indicators for evaluating the operational carbon emission levels of buildings and the content of carbon emission calculations, and introduces a specific operation mode for characterizing building energy consumption by the equivalent electricity method from the perspective of building energy consumption statistics. A calculation method for the carbon emission intensity per unit energy consumption of buildings based on the equivalent electricity method is constructed, and its validity is verified through calculations on 15 actual projects. The results show that the variances in carbon emission intensity per unit energy consumption index based on the equivalent electricity method are 0.03, 0.04 and 0.03 for residential, office and venue buildings, respectively, which are far lower than those of the carbon emission intensity per unit building area index (72.79, 123.33 and 153.35) and close to those of the building energy saving rate index (0.02, 0.01 and 0.02). Compared with the building energy saving rate and carbon emission intensity per unit building area, this index can better characterize the degree of building decarbonization and exhibits good evaluation stability across buildings of different functional types. Combined with the building energy consumption quota index, it is conducive to promoting the transformation of the construction sector from dual control of energy consumption to dual control of carbon emissions.

This study examines the relationship between carbon tax payments, electricity production costs, and carbon emission indicators in coal-fired power plants (PLTU) in Indonesia following the implementation of the carbon tax policy in 2022. Using post-implementation operational data, this study applies a descriptive and associative quantitative approach, acknowledging the potential endogeneity and mechanical relationships embedded in emission-based fiscal variables. The results indicate that carbon tax payments are not significantly associated with electricity production costs, suggesting that cost structures remain dominated by coal prices and operational efficiency. In contrast, a strong positive association is observed between carbon tax payments and carbon emission intensity, reflecting the mechanical linkage between emission-based taxes and emission indicators rather than policy effectiveness. These findings imply that the current carbon tax in Indonesia functions primarily as a fiscal and emission-reporting instrument, rather than an effective environmental control mechanism. The study highlights the need for complementary policies, technological upgrades, and more robust empirical designs to properly evaluate the environmental effectiveness of carbon taxation

The present study evaluates the effect of real-world operational factors and driving behaviors that significantly contribute to CO2 emissions and total energy consumption of the port-based heavy-duty vehicles (HDVs). Interpretable machine learning techniques are applied within an eXplainable Artificial Intelligence (XAI) framework to assess the impact of input variables on prediction accuracy. The inherent simplifications in these approaches often limit their ability to capture the complex, nonlinear characteristics of vehicular emission determinants, particularly under dynamic, micro-operational conditions associated with real-world settings. XGBoost showed higher predictive accuracy over conventional regression and other ensemble methods, with up to 46% improvement in R 2 and over 80% reduction in estimation errors. To address the black-box nature associated with the model, this study adopts XAI techniques, with SHapley Additive exPlanations (SHAP) employed to quantify feature contributions and enhance the interpretability. The results show that real-world CO2 emission levels remain substantially high under dynamic operational conditions, emphasizing the need for improved transit and freight management strategies to mitigate vehicular emissions. This further reinforces the importance of regulatory frameworks that incorporate CO2 emission and fuel-efficiency standards alongside conventional pollutant limits. Such progressive targets are intended to curb the climate impact, stimulate technological innovation, and support long-term low-carbon transition goals.

In this study, the environmental, economic, and social impacts of two renewable energy projects with the same installed capacity-Cardakli Hydropower Plant and Ekinozu Solar Power Plant, both located in Elazig Province, Turkey-were compared using real-case data. The projects were evaluated based on investment costs, energy output, operational efficiency, carbon footprint, and water usage. Key numerical findings include: annual energy production of 38.6 GWh for HEPP and 26.28 GWh for SPP, initial investment costs of $19.5million (HEPP) and $9.75million (SPP), and payback periods of 9.22 years for HEPP vs. 3.72 years for SPP. In terms of resource usage, HEPP consumes 191,544m³ water annually, while SPP requires only 8,672m³. Carbon emission intensity was calculated as 9 gCO₂/kWh for HEPP and 98-167 gCO₂/kWh for SPP. Based on these results, the solar power project was found to be more advantageous in terms of investment return, environmental impact, and sustainability, although hydropower produces more energy annually. This comparative evaluation contributes to the literature by integrating real operational data into feasibility analysis and offering insight for renewable energy planning in emerging economies.

Against the global push for “carbon peak and carbon neutrality” and Xinjiang’s role as a major arid-region agricultural base in China, balancing agricultural development with low-carbon transitions remains challenging due to its fragile ecology and resource-intensive farming. However, county-scale dynamics of cultivated land carbon emission intensity (CEI) and its drivers in Xinjiang are understudied, limiting targeted mitigation. This study analyzed Xinjiang’s cultivated land CEI (2000–2020) using the Geographically and Temporally Weighted Regression and Stochastic Impacts by Regression on Population, Affluence and Technology (GTWR-STIRPAT) model, geodetector, and spatiotemporal analysis, with counties as units. Data included 30 m-resolution land use data and socioeconomic statistics. Results showed CEI rose from 0.270 to 0.377 t/hm2, with marked spatial differences: northern Xinjiang saw fluctuating growth and a 58.65 km northeastward shift of emission gravity, while southern Xinjiang had lower western CEI (ecological constraints) and higher eastern CEI (agricultural expansion). Key drivers were total sown area (TSAC), agricultural film usage (UAPF), and rural agricultural population (RAP). Factor interactions (machinery power × sown area, q = 0.844) non-linearly amplified CEI. The GTWR-STIRPAT model (R2 = 0.97) outperformed OLS and captured heterogeneity—mechanization/area expansion dominated northern CEI, while film use/population mattered more in the south. Region-specific strategies are needed: northern Xinjiang should optimize machinery energy and control area expansion; southern Xinjiang, strengthen ecology and promote low-carbon inputs; eastern Xinjiang, leverage efficient oasis agriculture. This study supports precise carbon management in Xinjiang and similar arid regions globally.

ABSTRACT This study uses the directional distance function parameter estimation method to measure the MAC of carbon dioxide of the logistics industry in China’s 30 provinces from 2006 to 2021 and analyses its evolution characteristics. A panel data model is further constructed to examine its influencing factors. The results show that: (1) The MAC is generally showing a continuous growth trend, rising from 8971.29 yuan/ton in 2006 to 9496.18 yuan/ton in 2021. At the regional level, the MAC is highest in the central region, followed by the eastern and northeast regions, and lowest but fastest growing in the western region. At the provincial level, the MAC is relatively higher in economically developed regions, while those in inland regions are relatively lower. With the exception of Inner Mongolia and Shandong, which exhibit relatively pronounced peak characteristics, most provinces generally show an upward trend, although the growth patterns vary. (2) There is an inverted ‘U’ shaped relationship between the MAC and carbon emission intensity, with a critical value of 6.0634 tons/10,000 yuan for the carbon emission intensity. There is a negative correlation between the MAC and technological progress. Energy structure has a negative impact on the MAC, although this effect is not significant.

The multi-energy complementary power system achieves comprehensive and synergistic utilization of diverse energy sources, generating large-scale and distributed operational data. This introduces challenges in leveraging operational data for accurate and efficient carbon emission prediction. To effectively process the large-scale distributed operational data of power systems, identify key influencing factors, and achieve high-precision carbon emission prediction, this study investigates a carbon emission prediction method for multi-energy complementary power systems based on a multiple linear regression model. The structure of the multi-energy complementary power system is analyzed, and its carbon emission intensity is calculated. Based on the analysis results, preliminary selection of carbon emission influencing factors is conducted. A multiple linear regression model is constructed with the selected factors as independent variables and carbon emissions as the dependent variable. By performing significance tests on each independent variable, key influencing factors are identified, yielding an optimized multiple linear regression model. The model is integrated into the MapReduce parallel framework to expand computational scalability, enabling parallel processing of large-scale distributed power system data while ensuring prediction efficiency. The results demonstrate that the selected factor variables are reasonable, and the constructed prediction model exhibits a high goodness-of-fit. The prediction error ranges between 0.00516% and 0.00818%, confirming high accuracy and efficiency. The prediction results indicate that the experimental multi-energy complementary energy center’s carbon emissions increase annually from 2025 to 2031 and gradually decline from 2031 to 2034. These findings provide a scientific basis for formulating carbon emission reduction policies in multi-energy complementary power systems.

As an important ecological civilization pilot zone and free trade port in China， Hainan Province undertakes the important task of coordinated development of carbon reduction and economic development under the background of the implementation of the strategy of "carbon peak and carbon neutrality." Based on the calculation of carbon source， carbon sink， and net carbon emissions in Hainan Province from 2004 to 2023， the LMDI model and Lasso analysis were used to decompose and screen the influencing factors of carbon emissions in Hainan Province， and four Lasso-Transformer neural network models were included to predict carbon emissions in Hainan Province from 2024 to 2030. The results showed that： ① The trend of total carbon sink in Hainan Province from 2004 to 2023 was relatively stable， and the change trend of net carbon emission was basically consistent with the total carbon source. ② The main influencing factors of carbon emissions in Hainan Province were energy intensity， land carbon emission intensity， economic efficiency， land use structure， population size， and land use efficiency. ③ Through model optimization， the Lasso-PatchTST model was used to predict the carbon emission of Hainan Province from 2024 to 2030 and its influencing factors， and the carbon emission in 2030 was predicted to be 43，455，300 tons. The growth rate of land use efficiency factor was the fastest， and the growth rate of population size was the slowest. By optimizing industrial structure， improving resource utilization efficiency and strengthening ecosystem protection， it can promote the coordinated development of carbon reduction and economy in Hainan Province. The results of this study can provide a reference for decision-making of low-carbon economic development in Hainan Province.