Abstract:Based on China's energy resource endowments and guided by the "dual carbon" goals,this study focuses on promoting the green and low-carbon transformation of the coal industry through three core paths:technological breakthroughs,industrial collaboration,and institutional innovation.It systematically analyzes the functional positioning and core objectives of scientific and technological innovation in the coal sector towards 2035,clarifies phased key tasks,and in-depth explores the collaborative advancement system of scientific and technological innovation,including the implementation framework,participating entities,and landing paths.By optimizing the allocation of innovation resources,fostering a high-quality innovation ecosystem,and enhancing the effectiveness of innovation management,an efficient and collaborative scientific and technological innovation system is constructed to inject impetus into the high-quality development of the coal industry.In response to the development needs of scientific and technological innovation in the coal sector towards 2035,this study puts forward four key suggestions:Establishing a scientific and technological innovation monitoring and situation awareness platform,building an application-oriented "government-industry-university-research-user" collaborative innovation system,promoting the integrated planning of technological innovation and transformation paths,and improving innovation incentive mechanisms and institutional guarantees.By strengthening the multi-party collaborative linkage among governments,enterprises,universities,and research institutions,we will jointly promote the in-depth integration of scientific and technological innovation and low-carbon transformation,helping the coal industry to achieve green,low-carbon,and efficient sustainable development while consolidating energy security guarantees,and providing important support for the smooth realization of China's carbon peaking and carbon neutrality goals.

Abstract:Against the backdrop of global climate change and the carbon neutrality initiative,increasing attention has been paid to the spatial linkages of inter-provincial carbon emissions.This study constructs the spatial correlation network of China's inter-provincial carbon emissions from 2011 to 2021 using a gravity model,and examines both the overall-individual and regional-local network characteristics through social network analysis(SNA).Furthermore,the exponential random graph model(ERGM)is employed to explore the evolutionary dynamics of the carbon emission spatial network.The results reveal that China's inter-provincial carbon emission network has become increasingly interconnected,with an overall expansion in network scale.Core provinces such as Hebei,Jiangsu,Shandong,Hubei,and Henan play pivotal roles in regional carbon emission linkages.The southeastern coastal region constitutes a net-spillover cluster,the northern region forms a bidirectional spillover cluster,the central region functions as a broker cluster,while the western region constitutes a net-inflow cluster.The evolution of the spatial carbon emission network has gradually shifted from a chain-driven to a closure-driven structure.Empirical evidence further indicates that industrial upgrading,economic development,openness to the outside world,environmental regulation,and path dependence in the process of network formation are the key driving factors shaping the carbon emission network.

Abstract:Digital infrastructure plays an irreplaceable role in enhancing the green total factor productivity of industry.Based on the panel data of 30 provinces in China from 2010 to 2022,the impact of digital infrastructure on the green total factor productivity of industry was empirically analyzed using the bidirectional fixed effect and mediating effect model.The conclusion shows that digital infrastructure can effectively enhance the green total factor productivity of industry,and the conclusion still holds when the regression model is changed and special intervals are excluded for testing.The results of the mediating effect show that the optimization of industrial structure and green technological innovation are important channels for digital infrastructure to enhance the green total factor productivity of industry.The results of the heterogeneity test show that the improvement effect of digital infrastructure in the eastern region is more obvious.The improvement effect of the high zone of digital infrastructure is more obvious,and the inhibitory effect of the low zone of digital infrastructure is more obvious.Therefore,it is possible to further enhance the green technological innovation capabilities of high-growth areas in digital infrastructure,increase financial support and talent introduction for low-growth areas,avoid the low-end trap,and release the dividends of digital infrastructure through differentiated paths.

Abstract:In the context of global economic adjustment,the development of new quality productivity is an inherent requirement and an important focus to promote the high-quality development of the mining industry.Taking the listed companies in China's Shanghai and Shenzhen A-share mining industry from 2013 to 2023 as the research sample,the impact of new quality productivity on the high-quality development of the mining industry is empirically tested through the fixed-effect model,as well as the mediating role of technological innovation and the moderating role of government subsidies.The results show that the new quality productivity has a promoting effect on the high-quality development of the mining industry.Technological innovation has played a partly mediating role between new quality productivity and the high-quality development of the mining industry.Government subsidies negatively regulate the relationship between new productivity and technological innovation.The heterogeneity study finds that compared with the eastern and western regions,the promotion effect of new quality productivity on the high-quality development of the mining industry is more significant in the central region.Compared with state-owned enterprises,the new quality productivity has a more significant role in promoting the high-quality development of non-state-owned enterprises.The study reveals the internal mechanism between new quality productivity and the high-quality development of the mining industry,which can provide some enlightenment for related enterprises.

Abstract:Safe production is the core lifeline for the survival and development of coal enterprises.Its management system is essentially a complex system engineering involving multi-objective dynamic game.This paper takes 7 mines of Huali Company(covering open-pit and underground types,facing typical full-spectrum risks such as gas,water disasters,and roof collapses)as the research object,which systematically constructs and refines an efficient collaborative closed-loop management mechanism centered on "safety,prevention,treatment,technology,foundation,responsibility,strictness,and practicality".Through in-depth analysis of the practical paths of this mechanism in dimensions such as cultural infiltration,risk pre-control,targeted disaster governance,intelligent empowerment,grassroots strengthening,responsibility closure,regulatory rigidity,and execution implementation,it explains the internal logic of coal enterprises'coordinated advancement of high-level safety and high-quality development,and formulates a reusable universal paradigm for safety management in the industry.

Abstract:Coal mine safety is related to the overall situation of social stability,and national energy security.This article elaborates on the development course of coal mine safety in China,it has gone through several stages:a period of continuous rise in coal mine accidents after the founding;a period of high-level fluctuations;a period of rapid decline;and a period of low-level fluctuations and slow decline since the "14th Five-Year Plan" period.During this process,valuable experience and insights have been accumulated.Analyzes the close relationship between coal mine safety and economic social development,economic cyclical fluctuations,the intensity of safety supervision,and production order.To promote the development of coal mine safety,it is necessary to always take Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era as the fundamental guidance,regard coal mine safety as the top priority of work safety,take preventing and curbing major and extraordinarily serious accidents as the primary task,always maintain the sense of responsibility to earnestly implement policies,and always rely on the joint efforts of all parties.Based on this,suggestions for the development of coal mine safety during the 15th Five Year Plan period are proposed,we should scientifically set development goals,adhere to systematic governance,law-based governance,comprehensive governance,and source governance,make every effort to build a "five-in-one" work system for coal mine safety production,effectively prevent and curb major and extraordinarily serious accidents,and serve high-quality development with high-level coal mine safety.

Abstract:The advancement of GTI(green technology innovation)within China's coal and power industry chain significantly impacts the nation's progress in achieving energy conservation and emission reduction objectives.To study the GTI of enterprises in China's coal and power industry chain under the influence of heterogeneous environmental regulatory tools,this paper uses data from Chinese coal enterprises,electric power enterprises,and environmental protection enterprises spanning from 2011 to 2022 as a sample.This study combines organizational ecological theory with the bio-domain analysis methodology to analyze.Conclusions drawn:① In the coal power industry chain,government subsidies can inhibit the GTI ability of enterprises,resulting in a'crowding out'effect.Conversely,administrative penalties strengthen the GTI ability of enterprises,leading to a'forcing'effect.②The industrial policies implemented by the government on the coal-fired power industry chain have strengthened the environmental regulatory effect.③If the coal power industry chain first chooses to implement the policy of removing production capacity,it will effectively increase the likelihood of enterprises opting for GTI.

Abstract:This article focuses on the coal mining sector.It conducts an in-depth analysis of the economic efficiency leap driven by Artificial Intelligence in this field from a cross-industry perspective,innovatively introducing the "technology-economy transmission"methodology,which fills the gap in cross-industry economic benefit analysis in the mining industry.The research first establishes the benchmark of AI's economic promotion effect in five typical industries,namely the electronics manufacturing industry,the construction industry,the agricultural Internet of Things,the logistics and warehousing industry,and the semiconductor industry.It then builds a model for converting the economic value of safety benefits,quantifying that for every 1％increase in safety efficiency in the coal mining industry,the economic value gain is 1.6 times that of the manufacturing industry.It calculates that the AI transformation cycle for the coal mining industry is 5 to 7 years,with an annualized return rate of approximately 20％,and each mine can achieve annual personnel cost savings of 5 million yuan and production capacity premium of 45 million yuan.At the same time,it predicts that the coal mining industry will reach a turning point of benefit explosion three years after AI investment,and the energy security strategy gives it a policy weight coefficient advantage of 75％.The study examined model biases and inferential limitations arising from factors such as underground conditions,fragmented data protocols,and coal price fluctuations,providing a cross-industry comparative methodology and toolkit for AI transformation in the coal mining sector.

Abstract:Post-evaluation of science and technology projects can promote the transformation and application of scientific and technological achievements,improve the implementation efficiency of science and technology projects,and provide important support for China's strategy of high-level self-reliance and self-strengthening in science and technology.However,existing post-evaluation methods for science and technology projects still have significant limitations in dealing with input and output indicators that are unidentifiable or difficult to quantify.To address this issue,this paper integrates the DEA model without explicit inputs(DEA-WEI)with multi-attribute utility theory to construct a utility DEA-WEI dual model.Subsequently,this model is applied to evaluate the efficiency of State Grid science and technology projects,combined with a post-evaluation system that includes power-specific indicators such as technological maturity and advanced applicability.The efficiency evaluation results show that among the 28 State Grid science and technology projects,only 7 projects reached the efficiency frontier,while the remaining projects have significant room for improvement in intellectual property and technological innovation.The comparative analysis with the entropy weight method indicates that the utility DEA-WEI dual model constructed in this paper has obvious advantages over the entropy weight method.It can fully reveal the improvement paths of different projects in specific output indicators and provides a more and scientific practical basis for science and technology project management.

Abstract:As a new type of production factors,data factors play an critical role in promoting green and low-carbon development,and the marketisation of data factors also provides an important opportunity to improve the quality of corporate carbon disclosure.Based on the data of Chinese resource-based listed companies from 2013 to 2023,the impact of data factor marketisation on the quality of corporate carbon disclosure and its internal mechanism are empirically examined using a double difference model.It is found that data factor marketisation significantly improves the quality of carbon disclosure of resource-based listed companies,and the conclusion still holds after a series of robustness tests;data factor marketisation enhances the quality of corporate carbon disclosure through the enhancement of companies'green innovation technology and external supervision;the level of executives' green cognition strengthens the positive impact of data factor marketisation on the quality of carbon disclosure;and data factor marketisation Data factor marketisation has a more significant effect on the quality of carbon disclosure for companies in highly industrialized regions,highly open regions and low-competitive industries.

Abstract:The proposal of China's "dual-carbon" goals in 2020 has profoundly impacted the coal industry,where the synergy between corporate strategic decisions and ESG(Environmental,Social,and Governance)practices has become a critical factor influencing financial performance.Focusing on Shaanxi Coal Industry as a case study,this research analyzes the pathways through which ESG factors affect financial outcomes.By collecting financial data from 2019 to 2024,we establish a comprehensive evaluation system comprising 16 indicators across four dimensions:economic,environmental,social,and governance.The integration of the AHP-Entropy Weight Method addresses the limitations of traditional subjective weighting approaches,ensuring the scientific validity and accuracy of the assessment results.Applying this method to ESG performance research in the coal sector provides quantitative evidence for optimizing ESG resource allocation during the dual-carbon transition,offering valuable insights for promoting the sustainable and long-term green development of the coal industry.

Abstract:This study focuses on carbon emission efficiency assessment,aiming to serve the low-carbon transition in China's western regions and the national"dual carbon" goals.Employing a three-stage SBM-DEA model,the core methodology lies in utilizing Stochastic Frontier Analysis(SFA)to isolate the interference of environmental factors(per capita GDP,total import and export volume,R&D expenditure)and random noise,thereby precisely identifying the genuine management efficiency of each province.The results indicate that:Economic development(per capita GDP)simultaneously intensifies energy and capital input redundancy while suppressing carbon emission redundancy;Trade openness significantly reduces labor misallocation and carbon emission surplus;Conversely,R&D investment exacerbates energy and capital redundancy as well as carbon emissions.During 2011-2021,after isolating the interference of environmental factors and random noise,The landscape of carbon emission efficiency underwent significant changes:Gansu's average efficiency jumped from 0.549 to 0.981,representing a surge of 78.7％;Chongqing maintained full efficiency for 11 consecutive years,followed closely by Yunnan and Ningxia;Xinjiang emerged as the province with the lowest efficiency.Overall,the southwestern region demonstrates notable advantages in management efficiency,while the northwestern region faces greater pressure to improve efficiency,highlighting the disparities in genuine management levels across regions and their connection to policy implementation and technological adaptability.

Abstract:Based on the value chain theory,this study employs SWOT analysis to identify the internal and external conditions for intelligent coal mine construction.It constructs a systematic analysis framework of "intelligent construction-value chain reconstruction-competitive advantage" grounded in SWOT analysis,exploring the role of intelligent technology in value chain reconstruction and its impact on corporate competitive advantage.The research examines the phased characteristics of intelligent coal mine construction and the structural flaws of traditional value chains,then analyzes the driving forces and improvement pathways for value chain reconstruction from technological,policy,and market perspectives.The results demonstrate that intelligent construction significantly enhances cost control,differentiated competition,and dynamic capabilities of coal mining enterprises by promoting production digitization,supply chain coordination,service system extension,and green management systematization.It also fosters industrial chain collaboration and ecological expansion.From the dimensions of enterprise,industry,and government,this paper proposes strategies for advancing intelligent construction,asserting that value chain reconstruction driven by intelligent technology serves as the core pathway for coal mining enterprises to achieve sustainable competitive advantage and a critical direction for the energy industry's transition toward green,safe,and high-quality development.

Abstract:With the rapid development of digital technology and increasingly severe environmental pressure,driving green transformation through digital transformation is a necessary path for enterprises to achieve high-quality development.Based on the data of manufacturing companies listed on the Shanghai and Shenzhen A-shares from 2010 to 2023,this study explores the impact and mechanism of digital transformation on green transformation from the dual perspectives of external pressure and internal attention.Research has found that digital transformation of enterprises can significantly promote green transformation.After considering endogeneity issues and conducting robustness tests,the conclusion still holds true.Mechanism testing shows that digital transformation of enterprises promotes green transformation through dual channels of external pressure and internal attention.External pressure is reflected in strengthened government environmental regulations and increased public environmental attention,while internal attention is reflected in reduced cost stickiness and information asymmetry of enterprises.Heterogeneity testing found that the promotion effect of enterprise digital transformation on green transformation is more significant in high-tech enterprises,enterprises with higher environmental investment,and non heavy polluting enterprises.Expanded research shows that enterprise digital transformation has spatial spillover effect on green transformation.

Abstract:The "two joint operation pools" aims to solve the problem of operation mechanism in the "coal and electricity contradiction";Although the "coal and electricity joint operation" has played a certain role in alleviating the "coal and electricity contradiction",due to the limitations of the early mechanism and the lack of understanding of the nature of the joint operation,it has not been able to effectively reflect the systematic nature of the energy industry.We focus on the operation level of the coal power-electricity-renewable energy system,combs the position,classification and role of the"two joint operation pools",and analyzes the governance plan and mechanism framework of the"two joint operation pools".The study finds that"coal and electricity joint operation" can dredge the cost of coal and promote energy supply,but it cannot meet the demand of energy transformation;"Coal power and renewable energy joint operation" is in line with the dual objectives of energy supply and transformation,but there is no mature and effective operation mechanism at this stage;At present,it is necessary to build an effective and concrete "two joint operation pools" mechanism from the four dimensions of cost,scale,market and system.

Abstract:In order to improve the accuracy of the evaluation of the green development level of non-ferrous mineral enterprises,a green development level evaluation method based on combination weighting and matter-element model is proposed.Firstly,the evaluation index system for the green development level of non-ferrous mineral enterprises was constructed,comprising 4 first-level evaluation indicators and 14 second-level evaluation indicators.Secondly,to compensate for the limitations of single weighting methods,a combination of subjective and objective weighting was introduced to calculate the indicator weights,where in the Delphi method and the entropy weight method were employed to determine the subjective and objective weights,respectively.Subsequently,the optimal combination weights were computed using the combination weighting approach Finally,a comprehensive evaluation model was established by using the matter-element model,which was applied to two listed non-ferrous mineral companies.The results show that the evaluation results are consistent with the actual survey results,which verifies the validity and practicability of the model,and can provide a new evaluation method for the evaluation of the green development level of non-ferrous mineral enterprises.

Abstract:Family is an important practice unit of green consumption transformation and upgrading.Taking Chinese urban families as samples,based on the goal framing theory,this study explored the effects of normative motivation,hedonic motivation and gain motivation on household green purchasing behavior.Through path analysis of family life cycle and social status,we revealed the influence boundary of consumption motivations and their different effects among families.Empirical research shows that three consumption motivations in the goal framing theory have significant differential effects on household green purchasing behavior.First,normative motivation and hedonic motivation promoted household green purchasing behavior,while gain motivation played an inhibitory role.Second,family life cycle moderated the influence of three consumption motivations on household green purchasing behavior significantly.Specifically,normative motivation influence household green purchasing behavior more in Newlywed and Full nest Ⅱ family.Hedonic motivation has greater influence in Empty nestⅠfamily.And the effect of gain motivation is stronger in Single,Full nestⅠ,and Empty nestⅡfamily.Last,the results also showed that social status not only affected household green purchasing behavior directly,but also weakened the effect of family life cycle on the paths of consumption motivations.In light of these findings,this paper put forth differentiated green purchase promotion strategies for family units,offering novel insights for relevant departments seeking to advance green consumption at the family level.

Abstract:Against the backdrop of increasingly stringent carbon regulations,carbon risks are accelerating their penetration into enterprises'daily production and operations.Exploring the underlying mechanisms through which these risks influence corporate low-carbon technological innovation holds significant research value.This study examines the impact pathways of carbon risks on corporate low-carbon technological innovation using a two-way fixed effects model,with a sample comprising A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2022.The findings indicate that carbon risks exert a positive and promoting effect on corporate low-carbon technological innovation.Path analysis indicates that corporate environmental investments and dynamic adjustments to capital structure partially mediate the relationship between carbon risk and low-carbon technological innovation.Heterogeneity tests further reveal that the promotional effect of carbon risk on low-carbon technological innovation is more pronounced among non-state-owned enterprises and non-heavy-polluting enterprises.These findings provide empirical evidence for enterprises to adjust their low-carbon transition strategies,for governments at all levels to formulate carbon emission constraint standards,and for establishing robust long-term incentive and constraint mechanisms for enterprises.

Abstract:The continuous leap of productivity is the fundamental driving force for the progress of human society,and it is also the decisive force to drive the economic society to a higher form of evolution.As the core engine leading high-quality development,new-quality productivity takes scientific and technological innovation as the core,promotes the transformation of traditional industries to high-end,intelligent and green directions,and provides strong support for the optimization of economic structure and sustainable development.This paper focuses on the internal mechanism of new productivity enabling carbon neutrality governance,explains its multi-dimensional synergy in theoretical logic,and deeply analyzes the current technical bottlenecks,imperfect policy system,regional development differences and social cognition lag.On this basis,it puts forward specific paths such as promoting the optimization and upgrading of industrial chain supply chain,accelerating the development of clean energy technology,comprehensively promoting intelligent manufacturing,actively cultivating digital economy,strengthening ecological carbon sink capacity and environmental protection industry development,in order to provide theoretical reference and decision-making basis for building a practical system of deep integration of new productivity and carbon neutrality governance.

Abstract:Under the global climate governance and China's"dual carbon" strategy,accurate prediction of carbon trading prices is crucial for risk management and policy formulation.To address the limitations of traditional models in capturing unstructured information such as market sentiment and policy expectations,this study integrates online news text with structured time-series data to construct a multimodal carbon price prediction framework.Using the Shenzhen carbon market as a case study,we integrated 10 types of structured daily indicators and 1 893 carbon market-related online news articles from January 2018 to May 2025.Text mining techniques were employed to extract five topics and sentiment features,which were combined with time-series data to establish a cross-modal fusion mechanism.Comparative experiments were conducted using four deep learning models including long short-term memory(LSTM),bidirectional long short-term memory(BiLSTM),gated recurrent unit(GRU),and convolutional neural network(CNN).The findings indicate that multimodal data collaboration significantly enhances prediction performance by effectively capturing unstructured information like policy signals and market sentiment.The GRU model demonstrated the best performance,with its gating mechanism showing notable advantages in balancing computational efficiency and long-term dependency modeling.This study validates the value of unstructured online text data in carbon price prediction,and the proposed cross-modal fusion method provides a new paradigm for carbon price forecasting in complex market environments.Future research could extend this approach to multiple regional markets and incorporate multimodal data such as images and videos to enhance decision-support capabilities.