Global Carbon Emission Reduction Research Articles

Optimal Strategies for E-Commerce Platform Supply Chain: Carbon Emission Reduction and Financing

In the context of global carbon emission reduction (CER) targets and slowing economic growth, it is imperative for suppliers to make informed choices regarding CER and financing strategies. However, limited research has explored the impact of financing strategies on CER. This paper develops a supply chain model that includes a supplier, a manufacturer, an E-commerce platform (E-C platform), and consumers with a preference for low-carbon products. The supplier sets the wholesale price, while the manufacturer controls both the production quantity and the unit amount of CER. We examine whether the manufacturer will invest in CER with sufficient capital or under various financing scenarios, namely (1) traditional production with sufficient capital (Scenario ST); (2) CER implementation with sufficient capital (Scenario SG); (3) CER implementation with E-C platform financing (Scenario EG); (4) CER implementation with bank financing (Scenario BG). Through comparative analysis, the analysis reveals that, regardless of the financing method chosen, the supplier’s profit and the manufacturer’s production quantity increase when the manufacturer invests in CER technology innovation compared to the traditional scenario. Furthermore, in terms of the manufacturer’s profit, if the service cost of bank financing exceeds a certain threshold, the manufacturer should either seek financing from the E-C platform or abandon the CER investment. Additionally, with respect to CER outcomes, Scenario BG outperforms Scenario EG. These findings provide a theoretical foundation and decision-making support for supply chain participants when addressing carbon reduction and financing strategy decisions.

Explaining the changes in the green technology innovation efficiency of construction enterprises

Based on the objective of global carbon emission reduction, the green technology innovation efficiencies of construction enterprises (GTIE–CE) have attracted attention in various countries and regions worldwide. However, researchers have not yet assessed GTIE–CE from the perspectives of asymmetric innovation theory, resource orchestration theory and eco-innovation theory. To reveal the changes in GTIE–CE, an index system is constructed to measure this efficiency based on resource orchestration theory and eco-innovation theory. In addition, based on the asymmetric innovation theory, this study uses the spatial variance function and PVAR model to reveal the evolution mechanism of GTIE–CE with respect to market, government and technology dimensions. The main conclusions are as follows. (1) Regional heterogeneity is present in GTIE–CE in China, and the appearance of high efficiency spreads from the Yangtze River Delta and Pearl River Delta to the surrounding areas. (2) The market dimension positively affects the green technology innovation efficiency of construction enterprises, but this effect is delayed. (3) The government dimension significantly promotes the improvement in GTIE–CE, but this effect is delayed. This study provides an integrated theoretical perspective that reveals the mechanism of GTIE–CE, helps to broaden the research in the field of GTIE–CE, in addition to insights from China to further promote the green transformation of these enterprises.

Global Carbon Emission Reduction for Sustainable Development Research Hotspots and Frontiers – An Econometric Analysis Based on the Last 10 Years of WOS Literature

Global Carbon Emission Reduction for Sustainable Development Research Hotspots and Frontiers – An Econometric Analysis Based on the Last 10 Years of WOS Literature

Research on provincial implied carbon emissions in China under the shared responsibility driven by new quality productivity: A new approach

The allocation of responsibility for carbon emissions among involved parties has garnered significant attention. This study proposes a comprehensive sharing coefficient methodology known as CVAT (direct carbon emissions, value added, and technology level) to enhance the accuracy and equity of responsibility sharing driven by the new productivity standard. This study utilizes the environmental expansion multi-region input-output (EEMRIO) model to assess China's implicit provincial carbon emissions over the previous two decades. The study investigates the characteristics of the implied carbon transfer network using social network analysis (SNA) techniques. Key findings include: (1) The CVAT method's determination of responsibility sharing coefficients aligns with fairness and efficiency principles, resulting in a more equitable distribution of implied carbon emission responsibilities conducive to achieving fair carbon emission reduction. (2) Over the research period, national implied carbon emissions exhibited an upward trajectory, increasing from 3.305 billion tons in 2002 to 9.726 billion tons in 2017, with Shandong, Hebei, and Jiangsu provinces ranking among the top emitters in China. (3) Eastern coastal and western regions function as net carbon transfer out regions, while the central, northeast, and southwest regions act as net carbon transfer in regions. (4) The implied carbon transfer network demonstrated relative stability throughout the study period, with Shandong, Hebei, Jiangsu, and Inner Mongolia emerging as major carbon transfer provinces. The study's outcomes hold significant implications for the impartial and accurate assessment of carbon emission responsibilities and the advancement of global carbon emission reduction efforts.

Coordinated measurement of marine economy: High-quality and low-carbon development in China

The marine economy needs high-quality development, as severe greenhouse effect has become a global concern and carbon emission reduction has been an essential target of marine economy growth. However, a lack of synergistic research in the field of marine economy development and carbon emissions has hindered progress. This study explores the critical interactive relationship between the high-quality and low-carbon development in China's marine economy, aiming to foster green development in coastal urban agglomerations and enhance ecological security alongside economic growth. Despite the importance, comprehensive analysis across multiple dimensions of high-quality and low-carbon development of marine economy remains scarce. This paper establishes a systematic and scientific evaluation index system, utilizing text analysis technology and the Panel Vector Autoregression Model (PVAR) to quantitatively assess the complex system relationship between these dimensions. The results including: (1) The development of a 23 nodes tree structure through a bottom-up, multi-level coding method, effectively addressing the complexity of the current evaluation system for high-quality development of the marine economy. (2) Moran's I of subsystems are all negative, presenting an obvious high-low agglomeration characteristics. The discovery that intra-group differences exceed inter-group ones in marine economic development, indicates that the need for synergistic development among the provinces within marine economic circles. (3) The identification of significant bidirectional interactions between open and shared high-quality development and low-carbon development, with a notable lag effect in responses. The predominant role of innovation in all subsystems underscores the centrality of the marine innovation environment and factors as key drivers in the synergy of high-quality and low-carbon development in the marine economy.

The impact of green innovations on firm’s sustainable operations: Process innovation and recycling innovation

As the urgency and importance of global carbon emission reduction have escalated in recent years, numerous firms are considering ideas to develop innovative green technologies in order to contribute to the low-carbon economy. This paper examines two types of green innovations adopted by firms, namely process innovation (improving production processes to enhance energy efficiency) and recycling innovation (establishing a circular economy to recycle and re-manufacture waste products). By developing a game-theoretical model, our analytical results reveal the following findings. First, we find that both process and recycling innovations can attract more consumers and improve the firm’s profitability under specific conditions. Second, when the cost advantage of remanufacturing is sufficiently low, the adoption of process innovation is always more profitable than recycling innovation. Otherwise, the profitability largely depends on the trade-off between consumers’ perception of the two green innovations. Third, compared to a single innovation, the adoption of both process and recycling innovations (dual green innovation) leads to a further increase in consumer demand and firm’s profit when the cost advantage of remanufacturing exceeds a certain threshold. Furthermore, the optimal pricing set by the firm and consumer demand become more sensitive to consumers’ perception of greenness when dual green innovation is adopted. Finally, the equilibrium result suggests the existence of a “synergistic promoting effect” when the firm implements dual green innovation, which indicates that the advantages of each innovation are amplified when the firm adopts dual green innovation. These results can serve as guidelines for firms aiming to utilize green innovations in order to reduce carbon emissions.

Study on the development path of low-carbon retail clusters empowered by digital empowerment

This study provides an in-depth exploration of the mechanisms by which wholesale and retail clusters in China influence green and low-carbon synergistic development. Using the system generalized method of moments (SYS-GMM) method combined with the theory of industrial clusters, the contribution of digital technology application, green supply chain management, consumer behaviour, and corporate social responsibility to synergistic development is analyzed. It is found that the highly clustered wholesale and retail industry has promoted supply chain intelligence and logistics optimization through the extensive application of digital technology, creating favourable conditions for synergistic development. Regarding green supply chain management, cluster companies are more inclined to adopt green measures such as environmentally friendly packaging and energy-efficient logistics, effectively reducing carbon emission levels. In terms of consumer behaviour, the increase in the clustering rate has prompted consumers to be more inclined to consume green and purchase green products, thus promoting the decarbonization of products and services. CSR practices show that highly clustered companies are more active in implementing emission reduction measures, reflecting their responsibility for sustainable development. This study provides a new theoretical perspective for understanding the intrinsic mechanism of industrial clusters in carbon emission reduction and offers feasible carbon reduction strategies for industrial practices. In the urgent global carbon emission reduction context, this study provides essential academic and practical value for promoting China's wholesale and retail industry toward more sustainable development.

Low-carbon oriented planning of shared photovoltaics and energy storage systems in distribution networks via carbon emission flow tracing

With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed.

Combined multi-level context aggregation and attention mechanism method for photovoltaic panel extraction from high resolution remote sensing images

ABSTRACT In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model’s ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability.

Strategic Analysis of International Oil and Gas Companies’ Response to Global Carbon Emission Reduction Initiatives

This study investigates the role of oil and gas companies, particularly INDOPERTA, in mitigating global carbon emissions among rising energy demands and a shift toward coal and oil. This study highlights the urgency for strategic energy realignment and cleaner, sustainable sources, drawing from International Energy Agency (IEA) reports and various studies. The objective is to understand how companies such as INDOPERTA can integrate operations with effective carbon emission reduction strategies. A mixed-methods approach is employed, combining qualitative analysis of stakeholder perspectives with the AHP to prioritize initiatives based on identified criteria. Findings reveal compliance and regulation and leadership as critical areas needing focused resource allocation. The AHP analysis underscores the strategic importance of renewable energy investment and the pivotal role of leadership and innovation. It suggests a multifaceted approach involving regulatory adherence, international cooperation, and leadership development for effective carbon reduction. The study concludes that international oil and gas companies are at a crucial juncture. They must align their operations with global environmental goals and innovate to remain relevant in the climate agenda. It provides a roadmap for strategy realignment toward sustainability. However, this study has limitations, including its geographical focus, the dynamic nature of the industry, subjectivity in qualitative research and AHP, and reliance on secondary data. These findings highlight the need for ongoing research that adapts to industry changes and global carbon reduction efforts, ensuring that strategies remain relevant and effective.

Digitalization in prefabricated constructions and reducing carbon emission: empirical evidence from China.

Taking the implementation of digitalization in prefabricated constructions (DPC) as an exogenous shock, this study utilizes provincial panel data of China from 1997 to 2019, and uses the difference-in-differences model to identify the causal effect of DPC on carbon emissions in the construction industry (CECI). This study shows that DPC effectively reduces CECI with the results withstanding rigorous testing, including parallel trend tests, placebo tests, and a heterogeneous time-varying treatment test based on Bacon decomposition and the DIDm approach. The mechanism test confirms that DPC reduces CECI through two channels, which are improving existing building technologies and promoting better management capabilities. At the same time, further analyses show that the carbon emissions reduction effect of DPC is more pronounced in regions with a higher degree of marketization, financial development, human capital and environmental regulation. This study contributes to understanding the underlying mechanisms between DPC and CECI, providing new insights for the deeper integration of digital technologies and the construction industry. It offers new avenues for revitalizing the construction sector and facilitating achieving global carbon emissions reduction goals.

How do industrial land transfer modes impact carbon emissions? An intermediation perspective based on industrial structure.

Industrial land is currently the main carrier and important source of global carbon emissions, and as the world's largest developing country, China's large-scale and diversified industrial land supply has made it the world's largest carbon emitter. Therefore, researching the impact of different supply methods of industrial land on carbon emissions and its impact paths in China can help provide a reference for other countries to reduce carbon emissions from the perspective of urban industrial land management, which is of great significance for effectively promoting global carbon reduction. Based on this, this paper analyses the impact of different supply methods of industrial land on carbon emissions and its urban heterogeneity using the SYS-GMM and chain-mediated effects models for 285 cities in China from 2008 to 2020. The study found that, in general, the impact of different industrial land transfer modes on carbon emission has hysteresis and persistence. Agreement and listing transfer with government intervention can significantly exacerbate carbon emissions, while more market-based bidding and auction transfer can dampen carbon emissions. In terms of intermediary effects, the transfer of industrial land by agreement and listing will inhibit the rationalization and advancement of industrial structure, thus aggravating carbon emissions. The transfer of industrial land by bidding and auction will create barriers to entry and a crowding-out effect, promote the rationalization of industrial structure and the transformation and upgrading of industrial structure and moderate carbon emissions. In terms of city heterogeneity, there is urban heterogeneity in the impact of industrial land transfer on carbon emissions in cities with different economic types. Bidding and auction transfer for industrial land in both economically developed and less developed cities can promote carbon pollution. While the more developed urban economy makes the intermediary effect of industrial structure not significant. In the future, it is necessary to strictly control the scale of industrial land supply; the whole process supervision mechanism of industrial land allocation and differentiated industrial land supply strategies will provide useful experience for many developing countries in allocating industrial land to mitigate carbon emissions, generating effective contributions to global carbon emission reduction.

Does the "Belt and Road Initiative" benefit the environment? Insight from analysis of intra-industry trade in environment goods.

The expansion of the Belt and Road Initiative (BRI) has raised a wide range of concerns about its environmental impact. Therefore, from the perspective of environmental impacts, this study used the two-way fixed effect staggered differences in differences (TWFE Staggered DID) method to examine the impact of the BRI on the Environment Goods (EGs) intra-industry trade (IIT) between China and other Belt and Road (B&R) countries, including a sample of 191 countries, covering the period from 2010 to 2019 for eliminating the impact of COVID-19 and the financial crisis in 2008 and 2009. Because only 135 countries signed a Memorandum of Understanding between 2010 and 2019, this study treated these B&R countries as the study group, and the other 73 countries (non-B&R countries) as the control group. This study described EGs using the 54 6-digit code Environment Goods in Harmonized Commodity Description and Coding System listed in the "APEC LIST OF ENVIRONMENT GOODS" published by the Asia-Pacific Economic Cooperation in 2012, and used the intra-industry trade index proposed by Grubel and Lloyd in 1971 to measuring dependent variable. The research results indicated that the BRI has significantly promoted bilateral EGs IIT. The mechanism test implied that, in addition to direct impacts, the BRI also has indirect impacts by boosting the energy restructuring of B&R countries. These results prove that the BRI has positive impacts on the environment. The heterogeneity test showed that there is a heterogeneous impact depending on the type of IIT, product categorization, B&R countries' income levels, and geographic environment. This study not only gives theoretical and empirical evidence of the positive environmental impacts of the BRI, but also provides practical guidance for the development of EGS IIT between China and B&R countries, thereby contributing to global carbon emissions reduction and environmental governance to some degree.

Internal revenue sharing methodology for virtual power plant clusters considering carbon incentive and penalty mechanisms

With the rapid development of renewable energy and the urgent need for global carbon emission reduction, virtual power plants have become a high-profile energy management model that can integrate multiple energy resources. How to effectively integrate renewable energy to reduce carbon emissions, how to optimize the use of different energy resources, and how to fairly distribute economic benefits within virtual power plant clusters while encouraging the reduction of carbon emissions are issues that need to be addressed in research. The study first established a virtual power plant model and conducted in-depth optimization for its economic and environmental indicators. Subsequently, the study constructed a game model within the virtual power plant cluster, aiming to solve the problem of income distribution in this diversified energy system. The research results found that commercial users have the highest carbon emissions, followed by industrial users, while residential users have the lowest carbon emissions. In terms of optimized user electricity consumption behavior, the peak-to-valley difference rate of industrial users has been reduced by 17%, and the daily load rate has increased by 6%; the peak-to-valley difference rate of commercial users has been reduced by 12%, and the daily load rate has increased by 6%; The peak-to-trough difference rate for residential users decreased by 8%, and the daily load rate increased by 4%. In addition, the research also proposes a method of internal revenue distribution of virtual power plant clusters based on a carbon reward and punishment mechanism, which provides a new way for the synergy effects and economic benefit distribution of virtual power plants. Research is of positive significance in solving pressing issues in the field of energy management and provides strong support for the development of future sustainable energy systems.

Evaluation of optimal waste lithium-ion battery recycling technology driven by multiple factors

As energy and environmental issues become increasingly prominent, the process of global carbon emission reduction is now underway. In the transportation sector, the application of electric vehicles is an effective way to achieve energy saving and emission reduction. Lithium-ion battery, an important component of electric vehicles, has received widespread attention for its advantages including small size, high energy density and smooth discharge voltage. However, their potential safety and pollution issues are becoming prominent and must be properly handled. Moreover, the technical route and future direction of LIB recycling are still unclear at this stage. Herein, this paper evaluates different waste lithium-ion battery recycling technologies in a multi-criteria decision framework to determine the best technology. A criteria system driven by multiple factors is established, including environmental impact (C1), technical risk (C2), comprehensive resource utilization (C3), resource consumption (C4) and economic performance (C5). Subsequently, since the optimal technology is novel and difficult to select, the hesitant fuzzy linguistic term sets are used to express the degree of hesitation in expert decision making. Finally, from the aspects of the choice of sorting method, the Tomada de decisao interativa multicriterio method based on the expectation effect is used to evaluate the optimal waste lithium-ion battery recycling technology, and the validity of the framework is illustrated by data analysis.

Scientometric Analysis of Clean Energy Topics Based on Carbon Emissions

Net Zero stands as a crucial climate change metric and a significant stride towards accomplishing global carbon emission reduction targets. Carbon emissions, emanating are the release of carbon dioxide and other greenhouse gases due to human activities, underscore the urgency for action. Clean energy, characterized by its minimal environmental impact during production and usage, particularly in low greenhouse gas emissions, emerges as a pivotal solution in this trajectory. Hence, the advancement and application of clean energy technologies are paramount in the pursuit of global carbon emission reduction objectives and combatting climate change challenges. This study delves into the field of clean energy and carbon emissions, 461 articles in the Web of Science (WoS) database were analyzed. The findings from scientific metrology indicate a pronounced academic interest in clean energy and carbon emission discourse. Notably, the United. States leads to research contributions, closely followed by China. The publication sources mirror the interdisciplinary essence of the research domain, while key research themes encompass aspects such as consumption, energy utilization, and carbon emissions, among others. Research trends emphasize a concentrated exploration of decomposition strategies and CO2 emissions as pivotal focal points. In essence, a demand persists for enhanced scholarly investigations about climate change and ecological footprints, underlining many intricate considerations and a vast expanse for future developments.

Green outward foreign direct investment and host country environmental effects: The home country's carbon emission reduction system is crucial

Studies on the environmental impacts of outward foreign direct investment (OFDI) with less attention paid to the relationship between gradually increasing green investment and carbon emissions in the host countries. Based on the background of global carbon emission reduction, this study constructs a STIRPAT and difference-in-differences model to analyze the environmental effects of China's OFDI in ASEAN countries using unique enterprise-level data of China's OFDI from 2005 to 2020. The results show that the impact of Chinese OFDI on carbon emissions in ASEAN countries varies with the investment sector, the home country's enterprise ownership type, and environmental policy. China's green OFDI shows a clear “Pollution Halo” effect regarding investment sectors. However, the “Pollution Halo” effect of green OFDI by Chinese state-owned enterprises (SOEs) is more pronounced when considering the home country's enterprise ownership type. In addition, from the home country's environmental policy perspective, implementing China's domestic low-carbon pilot policy has a “Pollution Haven” effect on host countries. This study provides policy implications to promote the green transformation of Chinese OFDI and carbon emission reduction in ASEAN countries.

Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types

Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building’s length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption.

A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study

Electric vehicles (EVs) are part of the solution to achieve global carbon emissions reduction targets, and the number of EVs is increasing worldwide. Increased demand for EV charging can challenge the grid capacity of power distribution systems. Smart charging is therefore becoming an increasingly important topic, and availability of high-grade EV charging data is needed for analysing and modelling of EV charging and related energy flexibility. This study provides a set of methodologies for transforming real-world and commonly available EV charging data into easy-to-use EV charging datasets necessary for conducting a range of different EV studies. More than 35,000 residential charging sessions are analysed. The datasets include realistic predictions of battery capacities, charging power, and plug-in State-of-Charge (SoC) for each of the EVs, along with plug-in/plug-out times, and energy charged. Finally, we analyse how residential charging behaviour is affected by EV battery capacity and charging power. The results show a considerable potential for shifting residential EV charging in time, especially from afternoon/evenings to night-time. Such shifting of charging loads can reduce the grid burden resulting from residential EV charging. The potential for a single EV user to shift EV charging in time increases with higher EV charging power, more frequent connections, and longer connection times. The proposed methods provide the basis for assessing current and future EV charging behaviour, data-driven energy flexibility characterization, analysis, and modelling of EV charging loads and EV integration into power grids.

Digital transformation and manufacturing companies’ ESG responsibility performance

Digital transformation improves the efficiency and perception of companies. This paper investigates the impact of digital transformation in improving the environmental, social, and governance (ESG) responsibility performance of manufacturing companies in China. The results show that digital transformation can significantly improve manufacturing companies’ ESG responsibility performance. Every 1% increase in the degree of digital transformation will improve the ESG responsibility performance of manufacturing companies by 0.124%. Among the digital transformation techniques, digital strategy plays a more significant role than digital technology in improving manufacturing companies’ ESG responsibility performance. Total factor productivity, information transparency and investor stickiness moderate how digital transformation affects manufacturing companies’ ESG responsibility performance. The findings of this paper enrich and expand the research on the effects of digital transformation on manufacturing companies and provide a novel perspective to accelerate the progress of manufacturing companies worldwide in fulfilling ESG responsibilities and achieving global carbon emission reduction targets.