How does digitalization affect regional carbon emission efficiency? The role of carbon emission network

More precise accounting of marine carbon emission efficiency: Based on input-output analysis and considering marine carbon sinks

Green technology transfer is a vital pathway for optimizing innovation resources and advancing regional low-carbon transformation. Using green patent transfer data from 92 cities in China’s five major urban agglomerations during 2006–2023, this study employs two-way fixed-effects and mediation models to examine the spatiotemporal evolution of green technology transfer and its impact on carbon emission efficiency. The results show that: (1) Green technology transfer has expanded steadily. While local transfers remain dominant, inter-city transfers are rising, and the spatial pattern has evolved into a core–periphery structure with gradient diffusion and multi-center linkage. (2) Such transfer significantly improves carbon emission efficiency, with heterogeneous effects across regions. The Yangtze River Delta, Pearl River Delta, Middle Yangtze, and Chengdu–Chongqing agglomerations show the strongest effects. Within these regions, intra-agglomeration and inter-city transfers produce greater emission-reduction outcomes than intra-city transfers. (3) Green technology transfer indirectly improves carbon emission efficiency by upgrading industrial structures, strengthening urban innovation capacity, and enhancing resource allocation efficiency. This study explores the multidimensional mechanisms through which green technology transfer influences carbon emission efficiency at the urban-agglomeration scale and provides empirical evidence for optimizing regional green technology transfer patterns, promoting collaborative low-carbon and high-quality development, and supporting China’s dual-carbon goals.

Since the Industrial Revolution, the increasing emissions of greenhouse gases have posed unprecedented challenges to sustainable human development. As one of the most vital terrestrial ecosystems, farmland ecosystems play an irreplaceable role in balancing carbon emissions and absorption, attracting growing scholarly attention. Taking Jiangsu Province, one of China's major grain-producing regions, as the study area, this research integrates the Slacks-Based Measure (SBM) model, the entropy-weighted method, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to analyze the spatiotemporal evolution of farmland carbon effects-including carbon emissions, carbon absorption, and net carbon sequestration-during 2011-2021. Furthermore, a Grey Prediction Model was employed to forecast the carbon effects of 13 cities over the next 12 years. The results show that Jiangsu's farmland carbon emission efficiency exhibited an overall upward trend with fluctuations, with an average value of 0.76. The multi-year mean fitting degrees of resource input and agricultural output were relatively low, at 0.426 and 0.358, respectively, with substantial intercity differences. The average coupling coordination degree between resource input and agricultural output was 0.66, indicating a primary coordination state. The constructed GM (1,1) model achieved a qualification rate exceeding 73.80%, demonstrating its reliability for predicting farmland carbon effects. Forecasts suggest a potential weakening of the province's agricultural carbon sink effect, with the net carbon sequestration in 2033 expected to decline by 15.55% compared with the maximum value during the observation period. This study reveals the spatiotemporal characteristics and potential evolution patterns of farmland carbon effects, providing theoretical support for region-specific agricultural emission reduction policies and promoting the sustainable development of efficient, low-carbon agriculture.

Amid the rapid growth of the aviation sector, carbon reduction presents a significant challenge for airlines. This study investigates the structural characteristics and dynamic evolution of carbon emission efficiency among 18 global airlines from 2015 to 2021 using a two-stage super-efficient slack-based measure model (SBM) and an SBM-based Hicks–Moorsteen productivity index, incorporating absolute β-convergence tests. Key findings include the following: (1) The overall mean static efficiency of the airlines ranged from 0.225 (American Airlines) to 0.662 (Singapore Airlines), with an industry-wide average of 0.44. (2) Dynamic productivity change also exhibited significant variation: the overall mean superefficient SBM-based Hicks–Moorsteen (HM) productivity index was 0.962, but it dropped sharply to 0.526 in 2019–2020 due to the COVID-19 pandemic. After 2020, several airlines demonstrated significant recovery, with Emirates and Singapore Airlines achieving dynamic productivity change indices above 1.5. (3) In 16 out of 18 airlines, operational efficiency exceeded production efficiency, highlighting the importance of technological improvements in production. (4) Limited technological progress was identified as the main factor behind efficiency declines, while absolute β-convergence indicated that inefficient airlines are gradually catching up with efficient peers. These findings provide insights for airlines and policymakers in designing targeted carbon reduction strategies and promoting sustainable aviation development. The empirical scope of this study is limited to 18 major global airlines over the period 2015–2021. Due to data availability constraints, the sample does not fully cover all regions or low-cost carriers. The Hicks–Moorsteen index and its EC/TC components are used for interpretative and heuristic purposes only and should not be understood as a strict mathematical decomposition within the two-stage network SBM framework.

Urban polycentricity, spatial compactness, and carbon emission efficiency in China: An ensemble machine learning evaluation

Synergistic effects of manufacturing spatial agglomeration and carbon emission efficiency in urban hierarchies: Evidence from the Yangtze River Delta, China

Characteristics and influencing factors of the carbon emission efficiency association network of China's marine fishery industry

Carbon emission evaluation for regional public institutions operation phase based on improved LightGBM and carbon emission efficiency ratio

It is critical to the sustainable development of agricultural production that balances between increased grain yield with economic returns and carbon emissions in intensive oasis irrigation agroecosystems. Crop rotation and nitrogen management have been shown to have great potential in enhancing soil carbon sequestration and reducing carbon emissions. However, the comprehensive assessment of different planting systems in terms of crop productivity, economic benefits, soil carbon sequestration and greenhouse gas emissions remains unclear. A continuous field experiment was conducted since 2018 in the Hexi Oasis irrigation area, and data were collected from 2022 to 2025. Four planting patterns (W: spring wheat continuous cropping; WV: spring wheat-common vetch; WWV: spring wheat-winter wheat-common vetch; WRV: spring wheat-winter rapeseed-common vetch) and three nitrogen rates (N2: local conventional nitrogen amount, 360 kg ha −1 ; N1: 270 kg ha −1 , nitrogen amount reduced by 25%; N0: no nitrogen application, 0 kg ha −1 ) were set up as experiment treatments. The results showed that rotation significantly increased equivalent yield compared to continuous cropping. The cropping systems followed a decreasing yield order: WRV> WWV> WV. Compared to the spring wheat continuous cropping with local conventional nitrogen amount (WN2), the wheat-winter rapeseed-common vetch rotation combined with a 25% nitrogen reduction (WRVN1) significantly increased the equivalent yield by 10.7%. Furthermore, WRVN1 increased the soil organic carbon content by 3.9%, organic carbon stock, and carbon management index compared to WN2. In addition, crop rotation reduced carbon emissions (excluding WV), while the carbon emission efficiency of WV pattern was higher than W pattern. The carbon emission efficiency of WRVN1 was 24.2% higher than WN2 and had no significant difference with WRVN2. Furthermore, crop rotation increased net income (excluding WV). And net income of WRVN1 increased by 29.7% compared to WN2. Incorporating winter crops with leguminous green manure and moderate nitrogen reduction can achieve a trade-off between agronomic performance, economic benefits and ecological development in oasis irrigation districts. Our case provides pathways for N management and sustainable production practices in oasis irrigation agroecosystems. ● Diversifying continuous wheat with winter rapeseed and vetch increased system yield and farm net income in the Hexi Oasis. ● Winter crop–legume rotations with 25% less N maintained or increased wheat-equivalent yield while reducing soil CO₂ emissions. ● A spring wheat–winter rapeseed–vetch rotation with less N optimized trade-offs among yield, profit, and carbon sequestration.

In response to the dual challenges of global climate change and China's "dual carbon" goals, the digital economy has become increasingly vital in enhancing urban energy-related carbon emission efficiency. However, traditional studies have not fully considered its interregional network linkages and the resulting spatial spillover effects. To address this gap, this study employs panel data from 271 prefecture-level cities in China between 2011 and 2022 to construct a spatial correlation network of the digital economy. By integrating a modified gravity model, social network analysis, and spatial econometric techniques, we systematically examine the mechanisms, spatial heterogeneity, and spillover effects of this network on urban energy carbon emission efficiency. The findings reveal four main insights: (1) The spatial correlation network of China's urban digital economy demonstrates a complex and multi-threaded structure, with core cities such as Shanghai, Beijing, and Shenzhen dominating digital resource flows. Although overall carbon emission efficiency has improved, disparities across cities have widened. (2) An increase in network centrality significantly enhances energy carbon emission efficiency, with more pronounced positive externalities in the eastern region and in megacities. (3) Network centrality exerts significant spatial spillover effects on efficiency, exhibiting a boundary effect: the spillover coefficient peaks at 170km and decays with greater distance. (4) Urban innovation capacity serves as a key transmission channel in improving efficiency, whereas industrial upgrading currently imposes certain constraints, as the expansion of energy-intensive industries may inhibit short-term efficiency gains. These results provide practical implications for fostering spatially coordinated carbon reduction and improving urban energy carbon emission efficiency in China.

Improving carbon emission efficiency (CEE) in the construction sector is crucial for achieving low-carbon buildings and ecological civilization goals. To clarify the driving mechanisms of low-carbon development, this study measures the CEE of China's construction industry using the Super-SBM model and analyzes its spatiotemporal drivers via the Geographically Temporally Weighted Regression model. Based on the analysis, the approach enabled us to analyze temporal and regional variations in CEE across regions in 30 provinces in China from 2013 to 2022. It is found that the Carbon Emissions of the Construction Industry (CECI) experienced a stage from stable growth to slowing growth. From the spatial perspective, the CECI show a typical distribution pattern, which is higher in the eastern region, lower in the western region, and middle in the central region. Regarding the driving mechanisms, technical factors and demographic factors display distinct impacts. Technological advancement serves as a pivotal positive driver, enhancing efficiency through the dissemination of green construction technologies and energy-saving processes. Conversely, demographic factors generally impose constraints on CEE across most regions. This is primarily attributed to the escalating demand for infrastructure and the intensive resource consumption associated with population agglomeration. These results suggest that accelerating industrial upgrading in the construction sector could reduce reliance on high-carbon industries. The findings provide empirical evidence and policy insights for China's low-carbon transition, including differentiated regional strategies and enhanced interprovincial collaboration.

Impacts of digital economy on carbon emission efficiency: insights from development stages and spatial heterogeneity

ABSTRACT This paper utilizes micro-level data of Chinese manufacturing enterprises, sourced from the National Tax Survey Database (2007–2014), to measure carbon emission efficiency (CEE) for illustrating the stylized facts and identifying firm-level driving factors of CEE. Time-series analysis shows CEE increases during the sample period. Dynamic Olley-Pakes (OP) and logarithmic mean Divisia index (LMDI) decompositions indicate that technological innovation effect improves total emission efficiency, while resource allocation and firm exit effects play opposite role. Energy intensity and energy efficiency are primary driving forces of carbon emissions among distinct regions. Cross-sectional analysis finds that approximately 75% of CEE heterogeneity stems from intra-industry differences, with similar CEE provinces clustering spatially (high-high and low-low agglomerations), especially in high-output regions like the Yangtze River Delta. Empirical results show firm size, capital productivity and labor productivity positively correlate with CEE, whereas higher capital-labor ratios and export participation link to lower CEE.

Introduction The food cold chain logistics industry is a significant source of carbon emissions in food systems due to its heavy reliance on refrigerated power consumption. However, the mechanisms through which the digital economy promotes low-carbon transformation in this sector, particularly the mediating role of green technological innovation, remain insufficiently explored. Methods Using panel data from 30 provinces in China from 2013 to 2022, this study employs a non-radial, non-angular SBM-DEA model incorporating dual undesirable outputs (carbon emissions and food loss) to measure carbon emission efficiency. Two-way fixed effects models examine direct effects, bootstrap mediation tests assess indirect pathways, and Hansen panel threshold models evaluate the nonlinear moderating role of cold chain infrastructure. Results The digital economy significantly enhances carbon emission efficiency in food cold chain logistics, with an estimated coefficient of 0.124 ( p < 0.01). Green technological innovation partially mediates this relationship, with the indirect effect accounting for 40.32% of the total effect. A sequential mediation pathway of green technological innovation → food loss reduction → carbon emission efficiency improvement is confirmed. Cold chain infrastructure exhibits a threshold effect (γ = 0.418), with the marginal effect rising from 0.054 to 0.183 above the threshold. Heterogeneity analysis shows the carbon reduction effect is strongest in developed cold chain regions (0.197) and insignificant in lagging regions (0.041). Discussion These findings reveal that digital transformation promotes low-carbon development in food cold chain logistics through both direct efficiency gains and indirect innovation channels. The threshold effect highlights the complementary role of physical infrastructure in enabling digital carbon reduction. Targeted policy support for cold chain infrastructure investment, particularly in underdeveloped regions, is essential to unlock the full carbon reduction potential of the digital economy.

To address the challenge of balancing economic growth with carbon emission reduction, improving regional Carbon Emission Efficiency (CEE) has emerged as a central pathway to achieving the "dual carbon" goals. While most existing studies focus on inter-regional CEE linkages through pairwise interaction networks, such approaches fall short in capturing the high-order mechanisms of multi-regional collaboration. This study integrates the Super-SBM model with a modified gravity model to construct a CEE correlation network across 30 provincial administrative regions in China from 2007 to 2023. To overcome the limitations of traditional pairwise networks, simplicial complex theory is introduced to establish a high-order topological representation framework. Furthermore, by applying the multiorder Laplacian to assess the synchronization stability of the network, a directed second-order degree swap strategy is proposed to optimize its high-order structure. The findings reveal that the CEE correlation network has evolved from a single-pole aggregation pattern toward a multi-center equilibrium. Provinces with high connectivity play a dominant role in both pairwise and triadic synergies, though their collaborative advantages are gradually diffusing to central and western regions. Notably, with only a limited number (approximately five) of second-order degree swaps among key node pairs, the network's synchronization stability can be substantially improved. When first-order and second-order interaction strengths reach comparable levels (coupling strength α*≈0.5), the system achieves optimal resistance to external perturbations. This study highlights the pivotal role of high-order collaboration in shaping regional CEE linkages and offers a practical optimization pathway for structurally enhancing CEE through coordinated efforts in pursuit of the "dual carbon" goals.

Against the background of carbon neutrality and sustainable manufacturing, manufacturing faces multiple challenges in improving carbon emission efficiency, with the integration of digital technology and industrial internet platforms as a key solution. Given the digital technology availability, this study focuses on the internal mechanism and dynamic decision-making issues of how the industrial internet platform can enhance the manufacturing carbon emission efficiency. This study has constructed a differential game model involving manufacturing enterprises, the government, and industrial internet platforms. This study incorporates random interfering factors. This method addresses the limitations of neglected external uncertainties and overly loose assumptions. The innovation enables the capture of stochastic disturbances in carbon emission systems, such as market fluctuations and policy adjustments. The realism of matching equilibrium strategies to the enhancement of manufacturing carbon emission efficiency is improved. The findings are as follows: (1) Carbon emission system benefit coefficient, operational cost coefficient, and compliance cost coefficient negatively impact game strategies, while digital technology maturity coefficient has a positive effect; (2) Government subsidies under intermediate dependence enhance the effort of enterprises and platforms, and advanced symbiosis achieves optimal effort of the three subjects and system efficiency through in-depth digital technology availability empowerment; (3) The advanced symbiotic decision-making mechanism model is regarded as the optimal embodiment and practical application of the digital technology availability theory. Reasonable benefit distribution can fully unleash the potential value of digital technology availability.

Analysis of spatial heterogeneity and influencing factors of carbon emission efficiency at the provincial level in China based on machine learning.

Evaluating carbon efficiency across the lithium-ion battery industry using a SMOTE-augmented Super-SBM approach.

Construction enterprises are actively involved in engineering exploration, design, construction, and other activities that consume significant materials and resources. The development of a reliable method for measuring carbon emission efficiency (CEE) can help analyze their emission reduction potential, thus balancing economic growth with environmental sustainability. Along these lines, in this work, an enhanced three-stage data envelopment analysis slack-based measure model with undesirable outputs was employed to evaluate the CEEs of 55 publicly listed Chinese construction enterprises from 2017 to 2022. With the proposed methodology, various environmental influences are systematically eliminated through stochastic frontier analysis. Our analysis demonstrated substantial differences in CEEs between the first and third stages. Three environmental variables—economic development, total completed project value, and per capita output—were taken as determinants. After adjusting for these factors, the average CEE of construction enterprises over six years was found to be approximately 0.73, peaking in 2021, with significant room for improvement. According to the enterprise types, municipal installation enterprises exhibit the highest average efficiency (0.81), followed by decoration and renovation enterprises (0.78). Meanwhile, ecological landscaping enterprises show the greatest potential for emission reduction. China Communications Construction Co., Ltd., and China State Construction Engineering Corporation have maintained a leading position in CEEs in the industry for many years, while the CEEs of Shanghai Prosolar Resources Development Co., Ltd., Beijing New Space Technology Co., Ltd., and Shandong Meichen Science & Technology Co., Ltd., can only reach around 60%. Our work provides policymakers and industry leaders with empirical evidence for developing targeted decarbonization roadmaps in the construction sector, which is particularly valuable for emerging economies experiencing rapid urbanization.