Against the backdrop of green transformation and energy efficiency improvement in the manufacturing industry, machine tools, as the core equipment of industrial production, have become a key tool for reducing industrial carbon emissions through energy optimization. The spindle system, as the core component of a machine tool, accounts for 40%-60% of the total energy consumption during the cutting process. However, existing research mostly focuses on energy consumption during the cutting stage and lacks systematic modeling and quantitative evaluation of the inherent energy consumption characteristics of the spindle throughout the entire stage (standby, start-up, idle and braking). This article proposes a modeling method for the inherent energy consumption of a spindle system in all stages. By fitting experimental data, energy consumption functions for each stage are constructed, and an evaluation system is established that includes indicators such as standby power, expected idle power, and mass energy ratio. We conducted a validation study on the effectiveness of the model and the practicality of evaluation indicators for two vertical machining centers, VMC1165H and VMC855H. The results indicate that the research findings can provide quantitative basis for optimizing machine tool design and energy-saving regulation of process parameters and help improve energy efficiency and low-carbon transformation in the manufacturing industry.

Industry is the core sector with the most challenging task of carbon emission reduction in China. This paper explores how industrial transformation in China's industrial sector affects carbon emissions. We shed light on the effect of industrial sector transformation on carbon emissions, and divide industrial sector transformation into two aspects: industrial structure optimization and industrial spatial layout.We construct socioeconomic panel data for 30 cities in the urban agglomeration in the middle reaches of the Yangtze River from 2000 to 2020, and explore the influence of industrial transformation on carbon emissions in 33 industrial sectors in China from three dimensions: spatial spillover effect, spatiotemporal heterogeneity, and threshold effect. The results show that: (1) In the industrial structure optimization dimension, industrial rationalization has effect on decreasing carbon emissions, while industrial upgrading increases carbon emissions. In the industrial spatial layout dimension, industrial specialization agglomeration can decline carbon emissions, while industrial diversification agglomeration does not reduce carbon emissions but instead increase carbon emissions.(2)Both industrial structure optimization and industrial spatial layout have spatiotemporal heterogeneous impacts on carbon emissions during the study period.(3) Under different levels of economic development, industrial transformation will have different impacts on carbon emissions. In the future, the Chinese government should increase efforts to promote technological progress and innovation, taking technological progress and independent innovation capabilities as the central link in promoting industrial transformation. Those findings not only provide certain environmental research reference for China's industrial economic development and transformation, but also provide a practical reference for carbon reduction in other developing countries that are undergoing economic transformation.

This research uses Chongqing, China, as a representative case study to address the challenges inherent in investigating carbon peak pathways within topographically constrained inland Chinese cities. These challenges include the lack of regional structural variables, limited flexibility in scenario design, and a scarcity of case studies focusing on western China. Employing an extended STIRPAT model, the study systematically assesses the influence of critical regional factors-such as industrial structure, energy intensity, and energy mix-on carbon emissions. To improve the accuracy of parameter estimation and mitigate multicollinearity among variables, ridge regression was applied using data from China's Carbon Emissions Accounting Database (CEADs). Seven multi-scenario combinations were developed to project carbon emission trajectories from 2023 to 2050, followed by a comparative analysis with analogous studies conducted in Yunnan Province. The principal findings are as follows: (1) Population size, industrial structure, and energy mix constitute the primary determinants of carbon emissions in Chongqing; (2) Under the baseline scenario, carbon emissions are projected to peak in 2037, whereas adopting a "low-growth plus high-efficiency decarbonization" pathway could effectively advance the peak to 2035; (3) Relative to Yunnan-a similarly topographically constrained region in Southwest China-Chongqing exhibits more pronounced "valley industry" lock-in effects. Accordingly, mitigation strategies for Chongqing should emphasize accelerating the transformation of energy-intensive industries and enhancing regional energy coordination. This study illustrates how variations in industrial foundations lead to divergent carbon peak trajectories under comparable topographical constraints, thereby offering tailored policy insights for analogous regions.

The low-carbon transformation of the logistics industry is an important component of achieving China's "dual carbon" goals. Analyzing the dynamic evolution of carbon emissions in China's logistics industry and exploring effective paths for green and low-carbon development under the background of digital intelligence are of great significance for the long-term development of the logistics industry. Based on the carbon emission coefficient method, we calculated the carbon emissions of the logistics industry in 30 provinces and cities in China and analyzed the dynamic evolution of the logistics industry using kernel density analysis. We also constructed a measurement index system for the level of digitalization and incorporated it into the TOE framework, based on the three levels of technology, organization, and environment, to construct the antecedents of carbon emissions in the logistics industry, using the fsQCA method for configuration analysis of carbon emission reduction pathways in the logistics industry. The results indicate that: ① The carbon emissions of China's logistics industry showed a trend of first increasing and then decreasing, with a gradual decrease in the concentration of carbon emissions in the early stage and an increase in regional differences in carbon emissions, indicating spatial polarization. The gap between regions gradually narrowed in the later stage, and the distribution became more concentrated and balanced. ② There were three driving modes for low-carbon emissions in the logistics industry: digital intelligence-open collaborative type, technology-market synergy type, and digital intelligence-environment collaborative type. Among them, digital intelligence-open collaborative type was the most common. ③ There were three driving modes for non-low-carbon emissions in the logistics industry: open deficiency type, technology-open deficiency type, and numerical intelligence deficiency type. This was mainly due to the lack of two variables, namely the level of digital intelligence and the degree of openness to the outside world. ④ The presence of multiple configurational paths indicated that the level of digital intelligence was a core condition for the low-carbon development of China's logistics industry. Empowering the low-carbon transformation of the logistics industry with digital intelligence is a way to alleviate the burden of high-quality development in the logistics industry. The research findings can provide important reference and guidance for the government and relevant departments.

Under the dual carbon goals, reducing carbon emissions in the planting industry is crucial for achieving green and low-carbon transformation in agriculture. This study focuses on the planting industry in Heilongjiang Province, utilizing the LMDI model, an extended STIRPAT model, and ridge regression to measure carbon emissions from 2002 to 2021, identify influencing factors, and predict future carbon emissions. The results indicate that: ① From 2002 to 2021, carbon emissions showed an overall fluctuating upward trend, divided into four phases: a slow growth period from 2002 to 2004, an accelerated growth period from 2004 to 2016, a fluctuating decline period from 2016 to 2019, and a stable growth period from 2019 to 2021. ② Economic level and agricultural structure promoted carbon emissions, while production efficiency and labor scale inhibited them. ③ Future carbon emissions will maintain a slow growth trend. By 2031, carbon emissions were projected to reach 10.832 million tons, an increase of 593 500 tons compared to that in 2021, with an average annual growth rate of 0.53%. Although Heilongjiang Province has made initial progress in carbon emission reduction, future challenges remain. It is recommended to further develop practical carbon reduction strategies.

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.

In practice, the synergistic effect of reducing pollution and carbon emissions in the manufacturing industry still faces significant non-coordinated contradictions. Analyzing and strengthening the driving mechanism of synergistic efficiency in reducing carbon and pollution in the manufacturing industry is of great significance for promoting high-quality development of the manufacturing industry in a coordinated manner. Based on panel data of segmented manufacturing industries from 2011 to 2022, we identify the temporal evolution characteristics of non-coordinated coupling in carbon reduction, pollution reduction, and efficiency improvement in different industries and analyze their dynamic mechanisms through the XGBoost-SHAP model, which is conducive to accelerating the achievement of the "dual carbon" goal in the region. The results indicate that: ① The carbon reduction efficiency and pollution reduction efficiency of the manufacturing industry showed a phased improvement feature, but the differences within the industry were gradually widening. Among them, the carbon reduction efficiency has maintained steady growth, while the improvement of pollution reduction efficiency is relatively slow and faces greater challenges. ② Most manufacturing industries have maintained or improved a low-level non-coordinated coupling state in terms of carbon reduction, pollution reduction, and efficiency improvement, reflecting relatively good balance and progress characteristics. However, the non-coordinated coupling in industries such as chemical fibers, non-ferrous and black metal smelting, and rolling processing, as well as petroleum and coal, were showing an upward trend. ③ Ownership structure and environmental costs were key factors leading to the non-coordinated coupling of carbon reduction, pollution reduction, and efficiency improvement in the manufacturing industry, with the impact of ownership structure being the most significant. Although R&D intensity showed a negative effect, it is crucial for improving efficiency. ④ The tripartite synergy mechanism formed by high R&D intensity, appropriate market competition, and optimized capital allocation can effectively promote the coordinated and coupled development of carbon reduction, pollution reduction, and efficiency improvement in the manufacturing industry. Especially in fixed-asset intensive industries, the improvement of technological level has become the key to breaking through environmental cost constraints and overcoming technological substitution resistance.

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.

Photovoltaic (PV) technology is the core pathway foraddressingglobal climate change and advancing energy system decarbonization,yet the rapid expansion of PV manufacturing capacity has triggereda surge in life-cycle greenhouse gas emissions, sparking mountingconcerns. We integrated multisource heterogeneous data from China’sPV industry (2005–2024) to develop a life-cycle accountingframework, which quantifies industrial carbon emissions and theirevolutionary patterns across production stages and multiscale spatiotemporaldimensions. We also deconstructed the emission impacts of scale, technology,and structural factors, and predicted future trends. Over two decades,China’s PV industry-wide carbon emissions soared from 0.24to 205 million tonnes, while product-level emission intensity plummetedfrom 1,300 to 380 kg CO2eq/kWp. The contribution of technologicalprogress to emission reduction rose from about 3% of the observedincrease in emissions in 2005–2007 to nearly 100% in 2020–2024.Spatially, raw material and monocrystalline cell production have shiftedinland for cost advantages, while module assembly remains concentratedin coastal hubs like the Yangtze River Delta. Capacity utilization,grid decarbonization, and technical learning will dictate future emissions.Against surging global PV demand, coordinated capacity planning, acceleratedtech progress, optimized spatial distribution, and established incentivepolicies are pivotal to steering China’s PV manufacturing ontoa sustainable low-carbon path.

This study explores the impact of industrial intelligent transformation on industrial carbon emission efficiency and its spatial spillover effect, which is closely related to industrial sustainability. Based on panel data of 284 cities in China from 2011 to 2023, we find that intelligent transformation significantly improves urban industrial carbon emission efficiency, and reducing energy consumption intensity and promoting green technological innovation are two critical mediating channels. Moreover, both marketization level and environmental regulation stringency strengthen the promoting role of intelligent transformation on industrial carbon emission efficiency. Heterogeneity analysis demonstrates that the promotional effect of intelligent transformation on industrial carbon emission efficiency is strongest in Eastern China, followed by Central China, and weakest in Western China. In addition, this effect is significant in non-resource-based cities but insignificant in resource-based cities. Furthermore, intelligent transformation exerts a negative “competitive spillover effect” on industrial carbon emission efficiency of geographically adjacent cities, while generating a positive “demonstration spillover effect” on cities with similar economic development levels.

Does AI promote synergistic efficiency in reducing textile industry pollution and carbon emissions?

Revisiting the synergistic governance efficiency of industrial pollution and carbon emissions reduction from the perspective of eight comprehensive economic regions in China

To promote the low-carbon transformation of the shipping industry, this study explores the impact of the government’s reward and punishment mechanisms on carbon reduction behaviours in the shipping industry. Specifically, this study constructs a tripartite evolutionary game model among the government, shipping companies and port enterprises, and examines the factors involved. The main results of this study are as follows. First, government reward and punishment mechanisms have a significant effect on the sustainable development of the shipping industry. The probability of shipping companies and port enterprises adopting carbon emission reduction behaviour will rise when the government effectively implements the reward and punishment mechanism. Second, the regulatory cost has an important influence on the decision of the government. With a decrease in the cost of conducting government regulation, the government is inclined to adopt active regulation strategies. Third, the short operational time has adverse effects on the green transition of the shipping industry. However, when the ships’ operational time is long, shipping companies are inclined to adopt proactive carbon reduction strategies. Besides, shipping companies tend to prioritise local port enterprises for the refuelling of clean energy ships. Therefore, the probability of port enterprises building clean energy refuelling stations will rise when shipping companies choose to adopt clean energy ships. The aim of this study is to offer policy suggestions for mitigating carbon emissions in the shipping industry and to help stakeholders choose the relatively optimal strategy.

The rapid development of the lithium battery industry resulted in a large accumulation of spodumene mining residue (SMR). This paper explored the feasibility of using SMR as mineral admixtures in cement mortar. The properties of cement mortar, including flexural strength, compressive strength, fluidity, hydration characteristics, and durability, were studied. The interaction mechanism between SMR and cement mortar had been explored using the Dinger-Funk model, isothermal calorimetry, X-Ray Diffraction (XRD), fourier Transform Infrared Spectroscopy (FTIR), and thermogravimetry (TG) methods. Additionally, the environmental impact of cement mortar was quantitatively evaluated by the life cycle assessment method. The results showed that, while the dosage of SMR was no more than 20 wt.% replaced cement, the flexural strength, compressive strength, and anti-carbonation and sulfate corrosion resistance properties of S2 and S3 cement mortar were similar to that of the blank group. After curing for 28 d, the compressive strength of S1, S2, and S3 were 44.2 MPa, 43.15 MPa, and 40.32 MPa, respectively. SMR powder could improve the workability and reduce the cumulative hydration heat of cement mortar, which confirmed its application potential in large-volume concrete projects. The appropriate content of SMR incorporation into cement mortar could improve the structure and properties of cement-based materials through particle filling, the induced nucleation effect, and the pozzolanic effect. In addition, the utilization of SMR reduced the environmental emissions and resource consumption of cement-based materials. Using 1 m3 cement mortar as an example, for every 10 wt.% increase in SMR powder replacing cement, the energy consumption, the emissions of CO2, CO, CxHy, NOx, SO2, dust, and resource consumption of cement mortar were decreased by approximately 342 MJ, 40 kg, 8.1 g, 5.55 g, 88.3 g, 5.24 g, 1.80 kg, and 74.3 kg, respectively. The research findings of this paper are expected to promote the resource utilization of SMR and reduce the carbon emissions of the building materials industry.

This study presents a comprehensive comparative analysis of machine learning and statistical modeling approaches for monitoring and predicting Nigeria’s industrial CO2 emissions in support of the nation’s 2060 net-zero target. A two-phase experimental design was implemented: the first phase established baseline performance for five models, Linear Regression, Prophet, ARIMA, Support Vector Regression (SVR), and Random Forest, while the second phase utilized hyperparameter optimization to improve predictive robustness. The results indicate that Multiple Linear Regression (MLR) and optimized SVR demonstrated the highest predictive accuracy, achieving R2 values of 0.932 and 0.923, respectively, with an optimized SVR RMSE of 2.229 Mt CO2. In contrast, ensemble and univariate models, including Random Forest and ARIMA, exhibited weak generalization capacity with negative R2 values, indicating limited predictive validity when applied to a constrained longitudinal dataset (n = 52). Statistical diagnostics confirmed the transport sector as the most significant exogenous driver of industrial CO2 emissions in Nigeria. The proposed comparative modeling approach establishes a robust methodological basis for emission forecasting, with projected industrial CO2 emissions for 2025 estimated at 123.79 Mt CO2 (MLR). These findings align with the objectives of Nigeria’s National Industrial Decarbonization Plan (NIDP), supporting evidence-based model selection and strategic sectoral prioritization for achieving long-term emission reduction goals.

Research on the coupling coordination and interactive relationship between digital economy and carbon emissions in the construction industry

How industrial agglomeration and technological innovation affect carbon emission efficiency: Evidence from China

ABSTRACT As one of the world’s largest carbon emitters, China’s industrial sector plays a dominant role in the national emissions structure, making effective carbon reduction by industrial enterprises a critical task for addressing climate change. To systematically investigate the determinants of carbon emission reduction in industrial enterprises and their potential optimization pathways, this study adopts the Technology – Organization – Environment (TOE) framework and examines Chinese industrial firms using panel data from A-share listed companies over the period 2012–2023. A two-way fixed effects model, the GM(1,1) grey forecasting model are jointly employed for empirical analysis. The results indicate that: (1) within the TOE framework, green technological innovation, executives’ green cognition, media attention, government environmental subsidies, and industry competition significantly reduce carbon emission intensity; (2) heterogeneity analyses reveal substantial variations in these effects across regions, ownership structures, and firm life-cycle stages. Specifically, the mitigating effects of executives’ green cognition and media attention are more pronounced in non-state-owned enterprises, whereas government environmental subsidies and competitive mechanisms exhibit stronger emission-reduction effects in state-owned enterprises; (3) forecasts for the period 2024–2035 suggest that the overall carbon reduction performance of China’s industrial enterprises will continue to improve, although marked regional and sectoral disparities persist; and (4) carbon reduction potential analysis shows a highly uneven distribution of mitigation potential across regions and industries, with the eastern region and the manufacturing sector concentrating the largest reduction opportunities. The findings provide robust empirical evidence and theoretical support for the formulation of differentiated and targeted industrial carbon mitigation policies.

The creation of eco-friendly construction materials by utilising agro-industrial by-products presents a promising approach to lowering the carbon emissions of the building industry. This research explores the engineering behaviour of geopolymer concrete formulated with sugarcane bagasse ash, rice husk ash, and cow dung ash as aluminosilicate sources, further strengthened with basalt fibres in varying amounts (0-2.5%). A wide range of experiments was performed to assess mechanical performance (compressive, flexural, and split tensile strengths) and durability characteristics (chloride ion penetration, water absorption, and resistance to acid attack) over curing periods extending to 180days. Results exhibited a clear parabolic relationship with fibre content, identifying an optimum dosage near 1% basalt fibre. The compressive strength increased from 50 to 62MPa (24% improvement), flexural strength from 4.4 to 5.8MPa (32% increase), and split tensile strength from 3.7 to 4.8MPa (30% gain) at 180days. Durability indicators also improved at the optimum fibre dosage: water absorption decreased from 8 to 5%, acid attack-induced mass loss reduced from 38 to 6%, and RCPT values dropped from 3100 to 1600C, shifting chloride penetrability from moderate to low. However, fibre contents above 1.5% led to reduced workability, fibre clustering, and increased permeability. ANOVA confirmed the dominance of the quadratic fibre effect, with Fibre2 contributing 50-62% of the total variation across responses. Overall, the study demonstrates that basalt fibre-reinforced agro-waste geopolymer concrete not only satisfies structural and durability requirements but also advances sustainable construction by converting waste into resources and reducing dependency on Portland cement.

Under the "double carbon" goal， the green and high-quality development of the construction industry in China has an important impact on the realization of carbon peak. At the provincial scale， the IPCC coefficient method was used to estimate the carbon emissions of the construction industry from 2001 to 2020 in each province. Based on the STIRPAT model， the WOA-BP neural network model was used to simulate the carbon emissions of the construction industry and its spatio-temporal evolution from 2021 to 2050 in different scenarios. The research showed that： ① From 2001 to 2020， the per capita carbon emissions of China's construction industry will gradually increase， with high per capita carbon emissions in eastern and central provinces and low per capita carbon emissions in western and northern provinces. ② Population was the most important factor affecting the carbon emissions of the construction industry in each province from 2021 to 2050， and the influence was different among provinces. ③ From 2021 to 2050， the peak time of carbon emissions from the construction industry was different under different scenarios， and the peak time was the earliest under constrained scenarios. Seventeen provinces in the constrained scenario， five provinces in the normal equilibrium scenario， and zero provinces in the relaxed radical scenario were projected to achieve carbon peak before 2030. The north and southeast coastal areas reached the peak earlier， followed by Central China， Southwest late， and Shaanxi and Liaoning late. The western development strategy would delay the carbon peak of the western construction industry， so it is necessary to strengthen the carbon emission intensity constraint.